Datasets:
PeacefulData
/
Neko-v1

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xet
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483
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yeah but i was i was i was more thinking of the sort of the the whole browser framework as a running program now
[ "I was more thinking of the whole browser framework as a running program now.", "I was more thinking of the sort of the whole browser framework as a running program now.", "I was more thinking of sort of the whole browser framework as a running program now.", "Yeah, I was more thinking of the sort of the whole browser framework as a running program now.", "I was more thinking of that sort of the whole browser framework as a running program now." ]
['i was more thinking of the whole browser framework as a running program now', 'i was more thinking of the sort of the whole browser framework as a running program now', 'i was more thinking of sort of the whole browser framework as a running program now', 'yeah i was more thinking of the sort of the whole browser framework as a running program now', 'i was more thinking of that sort of the whole browser framework as a running program now']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "yeah.", "Yah.", "Yea." ]
['yeah', 'yes', 'yeah', 'yah', 'yea']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
like that
[ "by that.", "By that.", "like that.", "Like that.", "By that?" ]
['by that', 'by that', 'like that', 'like that', 'by that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "yeah.", "Yeah, yeah.", "Yeah," ]
['yeah', 'yes', 'yeah', 'yeah yeah', 'yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
it will be so much easier
[ "It will be so much easier.", "It'll be so much easier.", "It would be so much easier.", "it will be so much easier.", "it would be so much easier." ]
['it will be so much easier', 'it will be so much easier', 'it would be so much easier', 'it will be so much easier', 'it would be so much easier']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
why do we have to do it in memory
[ "Why do we have to do it in memory books?", "Why do we have to do memory books?", "Why do we have to do it memory books?", "Why do we have to do in memory books?", "Why do we have to do it in memory?" ]
['why do we have to do it in memory books', 'why do we have to do memory books', 'why do we have to do it memory books', 'why do we have to do in memory books', 'why do we have to do it in memory']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
certain
[ "So.", "So,", "so.", "Sorry.", "Sound." ]
['so', 'so', 'so', 'sorry', 'sound']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
is not
[ "Isn't it?", "isn't it?", "Isn't.", "Is it?", "Mm." ]
['is not it', 'is not it', 'is not', 'is it', '']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and then yeah th those are few per meeting and it well
[ "And yeah, there's a few per meeting.", "And yeah, there was a few per meeting.", "And yeah, there's a few per meeting and it, well.", "And yeah, there's a few per meeting, and it, well.", "And yeah, there's a few per meeting, and it's in well." ]
['and yeah there is a few per meeting', 'and yeah there was a few per meeting', 'and yeah there is a few per meeting and it well', 'and yeah there is a few per meeting and it well', 'and yeah there is a few per meeting and it is in well']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and then have that x m l nite x m l document tied to the interface
[ "And then have that XML document tied to the interface.", "And then have that night XML document tied to the interface.", "and then have that XML document tied to the interface.", "And then have that Knight XML document tied to the interface.", "And then have that Night XML document tied to the interface." ]
['and then have that xml document tied to the interface', 'and then have that night xml document tied to the interface', 'and then have that xml document tied to the interface', 'and then have that knight xml document tied to the interface', 'and then have that night xml document tied to the interface']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
like my experience with the british national corpus was that there is far more word types than you ever think because anything that is sort of unusual generally is a new word type
[ "Like my experience with the British National Corpus was that there's far more word types than you ever think because anything that's sort of unusual generally is a new word type.", "Like my experience with the British national corpus was that there's far more word types than you ever think because anything that's sort of unusual generally is a new word type.", "Like my experience with the British National Corpus was that there's far more word types than you ever think, because anything that's sort of unusual generally is a new word type.", "Like my experience with the British national corpus was that there's far more word types than you ever think, because anything that's sort of unusual generally is a new word type.", "Like my experience with the British National Corpus was that there's far more word types than you ever think because anything that's sort of unusual, generally is a new word type." ]
['like my experience with the british national corpus was that there is far more word types than you ever think because anything that is sort of unusual generally is a new word type', 'like my experience with the british national corpus was that there is far more word types than you ever think because anything that is sort of unusual generally is a new word type', 'like my experience with the british national corpus was that there is far more word types than you ever think because anything that is sort of unusual generally is a new word type', 'like my experience with the british national corpus was that there is far more word types than you ever think because anything that is sort of unusual generally is a new word type', 'like my experience with the british national corpus was that there is far more word types than you ever think because anything that is sort of unusual generally is a new word type']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
do you know
[ "Do you know that?", "Do you know that.", "do you know that?", "Did you know that?", "To you know that?" ]
['do you know that', 'do you know that', 'do you know that', 'did you know that', 'to you know that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
well more or less
[ "Well, more or less.", "well, more or less.", "Well more or less.", "well more or less.", "Well, More or less." ]
['well more or less', 'well more or less', 'well more or less', 'well more or less', 'well more or less']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but that that is enough data for the skimming and the the searching so what the searching does is the searching leaves the whole framework
[ "But that that's enough data for the skimming and the searching so what the searching does is the searching leaves the whole framework.", "But that that's enough data for the skimming and the searching. So what the searching does is the searching leaves the whole framework.", "But that that's enough data for the skimming. And the searching so what the searching does is the searching leaves the whole framework.", "But that that's enough data for the skimming. And the searching, so what the searching does is the searching leaves the whole framework.", "But that that's enough data for the skimming and the searching, so what the searching does is the searching leaves the whole framework." ]
['but that that is enough data for the skimming and the searching so what the searching does is the searching leaves the whole framework', 'but that that is enough data for the skimming and the searching so what the searching does is the searching leaves the whole framework', 'but that that is enough data for the skimming and the searching so what the searching does is the searching leaves the whole framework', 'but that that is enough data for the skimming and the searching so what the searching does is the searching leaves the whole framework', 'but that that is enough data for the skimming and the searching so what the searching does is the searching leaves the whole framework']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and i can even do the display to a vast degree without actually having their supplying framework working
[ "And I can even do the display to a vast degree without actually having the supplying framework working.", "And I can even do the display to a vast degree without actually having this applying framework working.", "And I can even do the display to a vast degree, without actually having the supplying framework working.", "I can even do the display to a vast degree without actually having the supplying framework working.", "And I can even do the display to a vast degree, without actually having this applying framework working." ]
['and i can even do the display to a vast degree without actually having the supplying framework working', 'and i can even do the display to a vast degree without actually having this applying framework working', 'and i can even do the display to a vast degree without actually having the supplying framework working', 'i can even do the display to a vast degree without actually having the supplying framework working', 'and i can even do the display to a vast degree without actually having this applying framework working']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
right
[ "Right.", "Great.", "Right?", "right.", "right?" ]
['right', 'great', 'right', 'right', 'right']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
if if utterances are split over 3 or 10 or w 100 different files
[ "If utterances are split over three or ten or one hundred different files.", "If utterances are split over three, or ten, or one hundred different files.", "If utterances are split over three or ten or a hundred different files.", "If utterances are split over three or ten or one hundred different files,", "If utterances are split over three, ten, or one hundred different files." ]
['if utterances are split over 3 or 10 or 100 different files', 'if utterances are split over 3 or 10 or 100 different files', 'if utterances are split over 3 or 10 or a 100 different files', 'if utterances are split over 3 or 10 or 100 different files', 'if utterances are split over 310 or 100 different files']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
to the automated topic topic segmentation
[ "To the automatic topic segmentation.", "to the automatic topic segmentation.", "To the automatic topics segmentation.", "To the Automatic Topic Segmentation.", "To the Automatic Topic segmentation." ]
['to the automatic topic segmentation', 'to the automatic topic segmentation', 'to the automatic topics segmentation', 'to the automatic topic segmentation', 'to the automatic topic segmentation']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
well the trouble with doing it on the word level is if you want the audio to synch up
[ "Well, the trouble with doing it on the word level is if you want the audio to sync up.", "Well the trouble with doing it on the word level is if you want the audio to sync up.", "Well, the trouble with doing it on the word level is, if you want the audio to sync up.", "Well the trouble with doing it on the word level is, if you want the audio to sync up.", "Well, the trouble with doing it on the Word level is if you want the audio to sync up." ]
['well the trouble with doing it on the word level is if you want the audio to sync up', 'well the trouble with doing it on the word level is if you want the audio to sync up', 'well the trouble with doing it on the word level is if you want the audio to sync up', 'well the trouble with doing it on the word level is if you want the audio to sync up', 'well the trouble with doing it on the word level is if you want the audio to sync up']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so we can just create an additional x m l file which for every utterance like the utterances have i d s i presume some references
[ "So we can just create an additional XML file, which for every utterance, like the utterances have ID's, I presume, some references.", "So we can just create an additional XML file which for every utterance, like the utterances have ID's, I presume, some references.", "So we can just create an additional XML file, which for every utterance like the utterances have ID's, I presume, some references.", "So we can just create an additional XML file which for every utterance like the utterances have ID's, I presume, some references.", "So we can just create an additional XML file, which for every utterance, like the utterances have ID's, I presume some references." ]
['so we can just create an additional xml file which for every utterance like the utterances have id is i presume some references', 'so we can just create an additional xml file which for every utterance like the utterances have id is i presume some references', 'so we can just create an additional xml file which for every utterance like the utterances have id is i presume some references', 'so we can just create an additional xml file which for every utterance like the utterances have id is i presume some references', 'so we can just create an additional xml file which for every utterance like the utterances have id is i presume some references']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "You know.", "You know?", "Yeah.", "you know.", "You know," ]
['you know', 'you know', 'yeah', 'you know', 'you know']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "yeah.", "Yeah,", "Yes.", "Yup." ]
['yeah', 'yeah', 'yeah', 'yes', 'yup']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
if not sure if it what it does to document it would probably make the yeah if if you have segments for that probably the
[ "Not sure if it what it does to document. It would probably make the yeah, if you have stagnants for that probably the", "Not sure if it what it does to document. It would probably make the yeah, if you have stagnants for that, probably the", "Not sure if it what it does to document. It would probably make the. Yeah, if you have stagnants for that probably the.", "I'm not sure if it what it does to document. It would probably make the. Yeah, if you have stagnants for that probably the.", "Yeah. Not sure if it what it does to document. It would probably make the yeah, if you have stagnants for that probably the" ]
['not sure if it what it does to document it would probably make the yeah if you have stagnants for that probably the', 'not sure if it what it does to document it would probably make the yeah if you have stagnants for that probably the', 'not sure if it what it does to document it would probably make the yeah if you have stagnants for that probably the', 'i am not sure if it what it does to document it would probably make the yeah if you have stagnants for that probably the', 'yeah not sure if it what it does to document it would probably make the yeah if you have stagnants for that probably the']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
like some primitive thing working next week
[ "Like some primitive thing working next week.", "Like, some primitive thing working next week.", "like some primitive thing working next week.", "Like some primitive thing working next week?", "Some primitive thing working next week." ]
['like some primitive thing working next week', 'like some primitive thing working next week', 'like some primitive thing working next week', 'like some primitive thing working next week', 'some primitive thing working next week']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "OK.", "Ok.", "Okay?", "Okay," ]
['okay', 'ok', 'ok', 'okay', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
we i think we have start and end times for words actually but it is yeah
[ "I think we have started end times for words actually, but it's we have.", "I think we have started end times for words actually but it's we have.", "I think we have started end times for words actually, but it's yeah.", "I think we have started end times for words, actually, but it's we have.", "We I think we have started end times for words actually, but it's we have." ]
['i think we have started end times for words actually but it is we have', 'i think we have started end times for words actually but it is we have', 'i think we have started end times for words actually but it is yeah', 'i think we have started end times for words actually but it is we have', 'we i think we have started end times for words actually but it is we have']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Mm.", "Hm.", "Hmm.", "Mhm.", "mm." ]
['', 'hm', '', '', '']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "OK.", "Ok.", "Okay,", "okay." ]
['okay', 'ok', 'ok', 'okay', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i am just thinking for this whole thing of like a different level sort of cutting out different different pieces
[ "I'm just thinking for this whole thing of like a different level sort of cutting out different pieces.", "I'm just thinking for this whole thing of like a different level sort of cutting out different different pieces.", "I'm just thinking for this whole thing of like a different level of cutting out different pieces.", "I'm just thinking for this whole thing of like a different level sort of cutting out different, different pieces.", "I'm just thinking for this whole thing of like a different level, sort of cutting out different pieces." ]
['i am just thinking for this whole thing of like a different level sort of cutting out different pieces', 'i am just thinking for this whole thing of like a different level sort of cutting out different different pieces', 'i am just thinking for this whole thing of like a different level of cutting out different pieces', 'i am just thinking for this whole thing of like a different level sort of cutting out different different pieces', 'i am just thinking for this whole thing of like a different level sort of cutting out different pieces']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
well wednesday
[ "Well, Wednesday.", "Well Wednesday.", "well Wednesday.", "well, Wednesday.", "Well, Wednesday. Yeah." ]
['well wednesday', 'well wednesday', 'well wednesday', 'well wednesday', 'well wednesday yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and then the actual file we can probably like copy from your home directory or something like it
[ "And then the actual file we can probably like copy from your home directory or something like it.", "And then the actual file, we can probably like copy from your home directory or something like it.", "And then the actual file we can probably, like, copy from your home directory or something like it.", "And then the actual file we can probably like, copy from your home directory or something like it.", "And then the actual file we can probably like copy from your home directory or something like that." ]
['and then the actual file we can probably like copy from your home directory or something like it', 'and then the actual file we can probably like copy from your home directory or something like it', 'and then the actual file we can probably like copy from your home directory or something like it', 'and then the actual file we can probably like copy from your home directory or something like it', 'and then the actual file we can probably like copy from your home directory or something like that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
compiled off line by a summary module
[ "compiled offline by a summary module.", "Compiled offline by a summary module.", "compiled offline by a summary module,", "compiled offline by a summary module", "compiled off line by a summary module." ]
['compiled offline by a summary module', 'compiled offline by a summary module', 'compiled offline by a summary module', 'compiled offline by a summary module', 'compiled off line by a summary module']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
there is just like for a baseline really
[ "There's just like for a baseline really.", "There's just like for a bass line really.", "There's just like, for a baseline really.", "There's just like for a base line really.", "There's just like for a baseline, really." ]
['there is just like for a baseline really', 'there is just like for a bass line really', 'there is just like for a baseline really', 'there is just like for a base line really', 'there is just like for a baseline really']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and and the utterances referenced to those topics i guess
[ "And the utterance is reference to those topics, I guess.", "And and the utterance is reference to those topics, I guess.", "And and the utterance is reference to those topics I guess.", "And the utterance is reference to those topics I guess.", "And and the utterances reference to those topics, I guess." ]
['and the utterance is reference to those topics i guess', 'and and the utterance is reference to those topics i guess', 'and and the utterance is reference to those topics i guess', 'and the utterance is reference to those topics i guess', 'and and the utterances reference to those topics i guess']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Mm.", "Yeah.", "Mhm.", "Yes.", "mm," ]
['', 'yeah', '', 'yes', '']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i just ordered
[ "It is ordered.", "I just ordered.", "I disordered.", "it is ordered.", "I disorder." ]
['it is ordered', 'i just ordered', 'i disordered', 'it is ordered', 'i disorder']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and that is separate from a summary of a segment within a meeting
[ "And that is separate from a summary of a segment within a meeting.", "and that is separate from a summary of a segment within a meeting.", "That is separate from a summary of a segment within a meeting.", "And that is separate from a summary of a segment, within a meeting.", "And that is separate from the summary of a segment within a meeting." ]
['and that is separate from a summary of a segment within a meeting', 'and that is separate from a summary of a segment within a meeting', 'that is separate from a summary of a segment within a meeting', 'and that is separate from a summary of a segment within a meeting', 'and that is separate from the summary of a segment within a meeting']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "yeah.", "Yeah, yeah.", "Yeah," ]
['yeah', 'yes', 'yeah', 'yeah yeah', 'yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
maybe you should send a support form
[ "Maybe you should send a support form.", "Maybe we should send a support form.", "Maybe you should send a report form.", "Maybe you should send a support form?", "maybe you should send a support form." ]
['maybe you should send a support form', 'maybe we should send a support form', 'maybe you should send a report form', 'maybe you should send a support form', 'maybe you should send a support form']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
they have if you s s hey you know
[ "They have a few SSA, you know.", "They have a few SSA you know.", "They have a few SSA, you know,", "They have a few SSA, you know?", "They have, if you SSA, you know," ]
['they have a few ssa you know', 'they have a few ssa you know', 'they have a few ssa you know', 'they have a few ssa you know', 'they have if you ssa you know']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but
[ "But.", "But,", "But", "but", "but." ]
['but', 'but', 'but', 'but', 'but']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i am not sure i what you mean
[ "I'm not sure what you mean.", "I'm not sure I what you mean.", "I'm not sure what what you mean.", "I'm not sure what I what you mean.", "I'm not sure uh what you mean." ]
['i am not sure what you mean', 'i am not sure i what you mean', 'i am not sure what what you mean', 'i am not sure what i what you mean', 'i am not sure what you mean']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah exactly
[ "Yeah, exactly.", "Yeah. Exactly.", "Yeah exactly.", "Yeah, Exactly.", "Yeah, I'm sorry." ]
['yeah exactly', 'yeah exactly', 'yeah exactly', 'yeah exactly', 'yeah i am sorry']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
like for example that like the summary of a meeting within the whole meeting corpus or meeting series y is meeting series a good word for that
[ "Like, for example, that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that.", "Like for example that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that.", "Like for example, that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that.", "Like, for example, that like the summary of a meeting within the whole meeting corpus, or meeting series, is meeting series a good word for that.", "Like, for example, that like the summary of a meeting within the whole meeting corpus or meeting series, is meeting series a good word for that." ]
['like for example that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that', 'like for example that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that', 'like for example that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that', 'like for example that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that', 'like for example that like the summary of a meeting within the whole meeting corpus or meeting series is meeting series a good word for that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yeah,", "yeah.", "Yes.", "Yea." ]
['yeah', 'yeah', 'yeah', 'yes', 'yea']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah we do not want it in to develop a little tree display as well for multiple results
[ "If you're not wanting to do the little tree display as well for multiple results.", "If you're not wanting to do the little tree display as well, for multiple results.", "if you're not wanting to do the little tree display as well for multiple results.", "And if you're not wanting to do the little tree display as well for multiple results.", "If you're not wanting to do a little tree display as well for multiple results." ]
['if you are not wanting to do the little tree display as well for multiple results', 'if you are not wanting to do the little tree display as well for multiple results', 'if you are not wanting to do the little tree display as well for multiple results', 'and if you are not wanting to do the little tree display as well for multiple results', 'if you are not wanting to do a little tree display as well for multiple results']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
icsi has some reasons for doing it
[ "Ixie had some reasons for doing it.", "Ixy had some reasons for doing it.", "Icksey had some reasons for doing it.", "Ixie has some reasons for doing it.", "Ixi had some reasons for doing it." ]
['ixie had some reasons for doing it', 'ixy had some reasons for doing it', 'icksey had some reasons for doing it', 'ixie has some reasons for doing it', 'ixi had some reasons for doing it']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i mean we have access to it here and i guess it probably means that we we can not give it to anybody else
[ "I mean, we have access to it here and I guess it probably means that we can't give it to anybody else.", "I mean, we have access to it here, and I guess it probably means that we can't give it to anybody else.", "I mean we have access to it here and I guess it probably means that we can't give it to anybody else.", "I mean, we have access to it here and I guess it probably means that we we can't give it to anybody else.", "I mean, we have access to it here. And I guess it probably means that we can't give it to anybody else." ]
['i mean we have access to it here and i guess it probably means that we can not give it to anybody else', 'i mean we have access to it here and i guess it probably means that we can not give it to anybody else', 'i mean we have access to it here and i guess it probably means that we can not give it to anybody else', 'i mean we have access to it here and i guess it probably means that we we can not give it to anybody else', 'i mean we have access to it here and i guess it probably means that we can not give it to anybody else']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "Okay,", "Ok.", "OK.", "okay." ]
['okay', 'okay', 'ok', 'ok', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
well some parts relevant for the search yes
[ "Will some parts relevant for the search? Yes.", "Were some parts relevant for the search? Yes.", "Will some parts relevant for the search, yes.", "Will some parts relevant for the search yes.", "Will some parts relevant for the search? yes." ]
['will some parts relevant for the search yes', 'were some parts relevant for the search yes', 'will some parts relevant for the search yes', 'will some parts relevant for the search yes', 'will some parts relevant for the search yes']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so that takes a lot of work away from us
[ "So that takes a lot of work away from us.", "That takes a lot of work away from us.", "So, that takes a lot of work away from us.", "So it takes a lot of work away from us.", "so that takes a lot of work away from us." ]
['so that takes a lot of work away from us', 'that takes a lot of work away from us', 'so that takes a lot of work away from us', 'so it takes a lot of work away from us', 'so that takes a lot of work away from us']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
is it too big
[ "Is it too big?", "is it too big?", "Is it too big.", "It is too big.", "Is it too big,?" ]
['is it too big', 'is it too big', 'is it too big', 'it is too big', 'is it too big']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i mean i guess we can
[ "I mean I guess we can.", "I mean I guess we can", "So I mean I guess we can.", "I mean, I guess we can", "I mean, i guess we can" ]
['i mean i guess we can', 'i mean i guess we can', 'so i mean i guess we can', 'i mean i guess we can', 'i mean i guess we can']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
that basically means our browser really is a viewer mostly
[ "That basically means our browser really is a viewer mostly.", "That basically means our browser really is a viewer, mostly.", "That basically means our browser is a viewer mostly.", "That basically means our browser really is a viewer. Mostly.", "that basically means our browser really is a viewer mostly." ]
['that basically means our browser really is a viewer mostly', 'that basically means our browser really is a viewer mostly', 'that basically means our browser is a viewer mostly', 'that basically means our browser really is a viewer mostly', 'that basically means our browser really is a viewer mostly']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so it is probably feasible
[ "So it's probably feasible.", "So, it's probably feasible.", "so it's probably feasible.", "So it's probably feasible", "It's probably feasible." ]
['so it is probably feasible', 'so it is probably feasible', 'so it is probably feasible', 'so it is probably feasible', 'it is probably feasible']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
maybe we could
[ "Maybe we could.", "Maybe we could?", "Maybe we could", "Maybe we could,", "maybe we could." ]
['maybe we could', 'maybe we could', 'maybe we could', 'maybe we could', 'maybe we could']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
chunks representing the individual meetings or some
[ "Chunks representing the individual meetings of some.", "chunks representing the individual meetings of some.", "Chunks, representing the individual meetings of some.", "Chunks representing the individual meetings at some point.", "Chunks representing the individual meetings of some people." ]
['chunks representing the individual meetings of some', 'chunks representing the individual meetings of some', 'chunks representing the individual meetings of some', 'chunks representing the individual meetings at some point', 'chunks representing the individual meetings of some people']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so we should basically make our own x m l document in memory
[ "So we should basically make our own XML document in memory.", "So, we should basically make our own XML document in memory.", "So we should basically make our own XML document and memory.", "So, we should basically make our own XML document and memory.", "So we should, basically, make our own XML document in memory." ]
['so we should basically make our own xml document in memory', 'so we should basically make our own xml document in memory', 'so we should basically make our own xml document and memory', 'so we should basically make our own xml document and memory', 'so we should basically make our own xml document in memory']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but it is probably yeah you probably have to work on word levels for importance
[ "but you probably have to work on word levels for importance.", "But you probably have to work on word levels for importance.", "but you probably have to work on word levels for importance", "but it's probably you probably have to work on word levels for importance.", "But it's probably you probably have to work on word levels for importance." ]
['but you probably have to work on word levels for importance', 'but you probably have to work on word levels for importance', 'but you probably have to work on word levels for importance', 'but it is probably you probably have to work on word levels for importance', 'but it is probably you probably have to work on word levels for importance']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and i think it is not possible to have just one class
[ "And I think it's not possible. To have just one class.", "And I think it's not possible. to have just one class.", "And I think it's not possible to have just one class.", "And I think, it's not possible. To have just one class.", "and I think it's not possible to have just one class." ]
['and i think it is not possible to have just one class', 'and i think it is not possible to have just one class', 'and i think it is not possible to have just one class', 'and i think it is not possible to have just one class', 'and i think it is not possible to have just one class']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
think they say quite a lot of numbers and before that there is this number
[ "I think they say quite a lot of numbers and before that there's this number.", "I think they say quite a lot of numbers and before that, there's this number.", "I think they say quite a lot of numbers and before that there's just numbers.", "I think they say um quite a lot of numbers and before that there's this number.", "I think they say, um, quite a lot of numbers and before that, uh, there's this number." ]
['i think they say quite a lot of numbers and before that there is this number', 'i think they say quite a lot of numbers and before that there is this number', 'i think they say quite a lot of numbers and before that there is just numbers', 'i think they say quite a lot of numbers and before that there is this number', 'i think they say quite a lot of numbers and before that there is this number']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah i w would then just not print out the start and end times
[ "Yeah, I would then just not print out the start and end times.", "Yeah, I would then just not print out the starting end times.", "Yeah, I would then just not print out the style nine times.", "Yeah, I would then just not print out the start and end time.", "Yeah, I would then just not print out the starting end time." ]
['yeah i would then just not print out the start and end times', 'yeah i would then just not print out the starting end times', 'yeah i would then just not print out the style 9 times', 'yeah i would then just not print out the start and end time', 'yeah i would then just not print out the starting end time']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
no
[ "You know.", "No.", "You know?", "you know.", "know." ]
['you know', 'no', 'you know', 'you know', 'know']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
what do you mean by diffe
[ "What do you mean by different?", "What do you mean by difference?", "What do you mean by this?", "What do you mean by different.", "What do you mean by that?" ]
['what do you mean by different', 'what do you mean by difference', 'what do you mean by this', 'what do you mean by different', 'what do you mean by that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
that you just combine them and then order the time stamps accordingly
[ "That you just combine them and then order the timestamps accordingly.", "that you just combine them and then order the timestamps accordingly.", "That you just combine them and then order the time stamps accordingly.", "that you just combine them and then order the time stamps accordingly.", "That you just combine them, and then order the timestamps accordingly." ]
['that you just combine them and then order the timestamps accordingly', 'that you just combine them and then order the timestamps accordingly', 'that you just combine them and then order the time stamps accordingly', 'that you just combine them and then order the time stamps accordingly', 'that you just combine them and then order the timestamps accordingly']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
it would probably find another number task quite easily seeing that it is a constrained vocabulary with a high co occurrence of the same 9 words
[ "You will probably find another number test quite easily seeing that it's a constrained vocabulary with a high co occurrence of the same nine words.", "You will probably find another number test quite easily, seeing that it's a constrained vocabulary with a high co occurrence of the same nine words.", "You will probably find another number test quite easily seeing that it a constrained vocabulary with a high co occurrence of the same nine words.", "You will probably find another number test quite easily, seeing that it a constrained vocabulary with a high co occurrence of the same nine words.", "You will probably find another number task quite easily, seeing that it's a constrained vocabulary with a high co occurrence of the same nine words." ]
['you will probably find another number test quite easily seeing that it is a constrained vocabulary with a high co occurrence of the same 9 words', 'you will probably find another number test quite easily seeing that it is a constrained vocabulary with a high co occurrence of the same 9 words', 'you will probably find another number test quite easily seeing that it a constrained vocabulary with a high co occurrence of the same 9 words', 'you will probably find another number test quite easily seeing that it a constrained vocabulary with a high co occurrence of the same 9 words', 'you will probably find another number task quite easily seeing that it is a constrained vocabulary with a high co occurrence of the same 9 words']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so i go one layer up
[ "So go one layer up.", "So I go one layer up.", "I go one layer up.", "So they go one layer up.", "So, they go one layer up." ]
['so go one layer up', 'so i go one layer up', 'i go one layer up', 'so they go one layer up', 'so they go one layer up']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
no
[ "Yeah.", "Mm.", "No.", "Mhm.", "Mm, No." ]
['yeah', '', 'no', '', 'no']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
f maybe the file number or something that is in the beginning
[ "Maybe the file number or something that is in the beginning.", "maybe the file number or something that is in the beginning.", "Maybe the file number or something, that is in the beginning.", "maybe the file number or something that is in the beginning,", "Maybe the file number, something that is in the beginning." ]
['maybe the file number or something that is in the beginning', 'maybe the file number or something that is in the beginning', 'maybe the file number or something that is in the beginning', 'maybe the file number or something that is in the beginning', 'maybe the file number something that is in the beginning']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "yeah.", "Yeah, yeah.", "Yeah,", "Yes." ]
['yeah', 'yeah', 'yeah yeah', 'yeah', 'yes']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and yeah so that
[ "And yeah, so that.", "And yeah so that.", "And, yeah, so that.", "And yeah. So that.", "And yeah, so that's." ]
['and yeah so that', 'and yeah so that', 'and yeah so that', 'and yeah so that', 'and yeah so that is']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
how big is it without
[ "How big is it without, um,", "How big is it without um,", "How big is it, without um,", "How big is it, without, um,", "How big is it without um?" ]
['how big is it without', 'how big is it without', 'how big is it without', 'how big is it without', 'how big is it without']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but on algorithmic level whether we actually whether there is some way to just represent meetings as as topics
[ "But on an algorithmic level, whether we actually, whether there's some way to just represent meetings as topics.", "But on an algorithmic level, whether we actually whether there's some way to just represent meetings as topics.", "But on an algorithmic level, whether we actually, whether there's some way to just represent meetings as, as topics.", "But on an algorithmic level, whether we actually whether there's some way to just represent meetings as as topics.", "But on an algorithmic level, whether we actually, whether there's some way to just represent meetings as as topics." ]
['but on an algorithmic level whether we actually whether there is some way to just represent meetings as topics', 'but on an algorithmic level whether we actually whether there is some way to just represent meetings as topics', 'but on an algorithmic level whether we actually whether there is some way to just represent meetings as as topics', 'but on an algorithmic level whether we actually whether there is some way to just represent meetings as as topics', 'but on an algorithmic level whether we actually whether there is some way to just represent meetings as as topics']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and then sort of with that knowledge we should be able to then say okay that type of nite x m l data we want to load into it
[ "And then sort of with that knowledge, we should be able to then say, okay, that type of Night XML data we want to load into it.", "And then sort of with that knowledge, we should be able to then say, okay, that type of Night XML data, we want to load into it.", "And then, sort of with that knowledge, we should be able to then say, okay, that type of Night XML data we want to load into it.", "And then sort of with that knowledge, we should be able to then say, okay, that type of NADXML data we want to load into it.", "And then, sort of with that knowledge, we should be able to then say, okay, that type of Night XML data, we want to load into it." ]
['and then sort of with that knowledge we should be able to then say okay that type of night xml data we want to load into it', 'and then sort of with that knowledge we should be able to then say okay that type of night xml data we want to load into it', 'and then sort of with that knowledge we should be able to then say okay that type of night xml data we want to load into it', 'and then sort of with that knowledge we should be able to then say okay that type of nadxml data we want to load into it', 'and then sort of with that knowledge we should be able to then say okay that type of night xml data we want to load into it']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so it is not that crucial
[ "So it's not that crucial.", "So, it's not that crucial.", "So it is not that crucial.", "so it's not that crucial.", "So, it is not that crucial." ]
['so it is not that crucial', 'so it is not that crucial', 'so it is not that crucial', 'so it is not that crucial', 'so it is not that crucial']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so b e d or b b d or something and that is always the same group
[ "So BED or BBD or something, and that's always the same group.", "So BED or BBD or something and that's always the same group.", "So B E D or B B D or something, and that's always the same group.", "So BED or BBD or something. And that's always the same group.", "So B, E, D, or B, B, D, or something, and that's always the same group." ]
['so bed or bbd or something and that is always the same group', 'so bed or bbd or something and that is always the same group', 'so b e d or b b d or something and that is always the same group', 'so bed or bbd or something and that is always the same group', 'so b e d or b b d or something and that is always the same group']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "OK.", "Ok.", "Okay, okay.", "Okay," ]
['okay', 'ok', 'ok', 'okay okay', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i am not quite sure i have only seen that the the individual words have got an i d
[ "I'm not quite sure. I have only seen that the individual words have got an ID.", "I'm not quite sure I have only seen that the individual words have got an ID.", "Ah, I'm not quite sure. I have only seen that the individual words have got an ID.", "Uh, I'm not quite sure. I have only seen that the individual words have got an ID.", "Uh, I'm not quite sure. I have only seen that the, the individual words have got an ID." ]
['i am not quite sure i have only seen that the individual words have got an id', 'i am not quite sure i have only seen that the individual words have got an id', 'ah i am not quite sure i have only seen that the individual words have got an id', 'i am not quite sure i have only seen that the individual words have got an id', 'i am not quite sure i have only seen that the the individual words have got an id']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yeah,", "yeah.", "Yes.", "Yea." ]
['yeah', 'yeah', 'yeah', 'yes', 'yea']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "Ok.", "OK.", "Okay,", "Okay, okay." ]
['okay', 'ok', 'ok', 'okay', 'okay okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
0 yeah yeah we will need that
[ "Oh yeah, yeah, we need that.", "Oh yeah, yeah, wouldn't eat that.", "Oh yeah, yeah, we'll need that.", "Oh yeah. Yeah, I would need that.", "Oh yeah, yeah, it wouldn't eat that." ]
['0 yeah yeah we need that', '0 yeah yeah would not eat that', '0 yeah yeah we will need that', '0 yeah yeah i would need that', '0 yeah yeah it would not eat that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay so i will just meet you in in 18 a in the afternoon
[ "Okay so I'll just meet you in in eighteen in the afternoon.", "Okay, So I'll just meet you in in eighteen in the afternoon.", "Okay, so I'll just meet you in in eighteen in the afternoon.", "Okay, So I'll just meet you in eighteen in the afternoon.", "Okay. So I'll just meet you in in eighteen in the afternoon." ]
['okay so i will just meet you in in 18 in the afternoon', 'okay so i will just meet you in in 18 in the afternoon', 'okay so i will just meet you in in 18 in the afternoon', 'okay so i will just meet you in 18 in the afternoon', 'okay so i will just meet you in in 18 in the afternoon']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "Ok.", "OK.", "Okay,", "okay." ]
['okay', 'ok', 'ok', 'okay', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so give m give me different files as long as like it m if you could name them in a way that is easy to enumerate over them
[ "So give give me different files as long as like if you could name them in a way that is easy to enumerate over them.", "So give give me different files as long as like if if you could name them in a way that is easy to enumerate over them.", "So give give me different files as long as, like, if you could name them in a way that is easy to enumerate over them.", "So give give me different files as long as like, if you could name them in a way that is easy to enumerate over them.", "So give give me different files as long as, like if you could name them in a way that is easy to enumerate over them." ]
['so give give me different files as long as like if you could name them in a way that is easy to enumerate over them', 'so give give me different files as long as like if if you could name them in a way that is easy to enumerate over them', 'so give give me different files as long as like if you could name them in a way that is easy to enumerate over them', 'so give give me different files as long as like if you could name them in a way that is easy to enumerate over them', 'so give give me different files as long as like if you could name them in a way that is easy to enumerate over them']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but i do not know i mean that
[ "But I don't know.", "But I don't know, I mean, that", "But I don't know. I mean, that", "But I don't know, I mean that.", "But I don't know. I mean, that." ]
['but i do not know', 'but i do not know i mean that', 'but i do not know i mean that', 'but i do not know i mean that', 'but i do not know i mean that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
0
[ "Yeah.", "Mm.", "Hm.", "Yes.", "Hmm." ]
['yeah', '', 'hm', 'yes', '']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
like over the whole corpus sort of
[ "Like over the whole corpus sort of.", "Like, over the whole corpus sort of.", "like over the whole corpus sort of.", "Like, Over the whole corpus sort of.", "Like over the whole corpus, sort of." ]
['like over the whole corpus sort of', 'like over the whole corpus sort of', 'like over the whole corpus sort of', 'like over the whole corpus sort of', 'like over the whole corpus sort of']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but depending on the context the size and what we consider a document in the sense of calculating t f i d f is going to change
[ "But depending on the context, the size and what we consider a document in the sense of calculating TF IDF is going to change.", "But depending on the context, the size, and what we consider a document in the sense of calculating TF IDF is going to change.", "But depending on the context, the size and what we consider a document in the sense of calculating TF, IDF is going to change.", "But depending on the context, the size and what we consider a document in the sense of calculating TF IDF is gonna change.", "But, um, depending on the context, the size and what we consider a document in the sense of calculating TF IDF is going to change." ]
['but depending on the context the size and what we consider a document in the sense of calculating tf idf is going to change', 'but depending on the context the size and what we consider a document in the sense of calculating tf idf is going to change', 'but depending on the context the size and what we consider a document in the sense of calculating tf idf is going to change', 'but depending on the context the size and what we consider a document in the sense of calculating tf idf is going to change', 'but depending on the context the size and what we consider a document in the sense of calculating tf idf is going to change']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
why w
[ "Right.", "Alright.", "All right.", "Huh.", "Right," ]
['right', 'alright', 'all right', 'huh', 'right']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i mean if you think if it is r roughly a 1000000 words and 9 characters per word sounds realisti
[ "I mean, if you think if it's roughly a million words and nine characters per word sounds realistic.", "I mean if you think if it's roughly a million words and nine characters per word sounds realistic.", "I mean, if you think if it's roughly a million words, and nine characters per word sounds realistic.", "I mean, if you think if it's roughly a million words and nine characters per word sound realistic.", "I mean, if you think, if it's roughly a million words and nine characters per word sounds realistic." ]
['i mean if you think if it is roughly a 1000000 words and 9 characters per word sounds realistic', 'i mean if you think if it is roughly a 1000000 words and 9 characters per word sounds realistic', 'i mean if you think if it is roughly a 1000000 words and 9 characters per word sounds realistic', 'i mean if you think if it is roughly a 1000000 words and 9 characters per word sound realistic', 'i mean if you think if it is roughly a 1000000 words and 9 characters per word sounds realistic']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "True.", "Good.", "Do you?", "Do you know what?" ]
['okay', 'true', 'good', 'do you', 'do you know what']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so
[ "So,", "So.", "So", "So HM", "So HM." ]
['so', 'so', 'so', 'so hm', 'so hm']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
maybe we should have like a cut off
[ "Maybe we should have like, um, a cut off.", "Maybe we should have like, um, a cutoff.", "Maybe we should have like a cut off.", "Maybe we should have like a cutoff.", "Maybe we should have, like, um, a cut off." ]
['maybe we should have like a cut off', 'maybe we should have like a cutoff', 'maybe we should have like a cut off', 'maybe we should have like a cutoff', 'maybe we should have like a cut off']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah or or ready made versions of them for that matter and
[ "Yeah, or ready made versions of them for that matter.", "Yeah or ready made versions of them for that matter.", "Yeah. Or ready made versions of them for that matter.", "Yeah, or or ready made versions of them for that matter.", "Yeah, or ready made versions of them for that matter, and." ]
['yeah or ready made versions of them for that matter', 'yeah or ready made versions of them for that matter', 'yeah or ready made versions of them for that matter', 'yeah or or ready made versions of them for that matter', 'yeah or ready made versions of them for that matter and']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
ah okay
[ "Okay.", "Ah, okay.", "Uh, okay.", "Ah okay.", "Oh, okay." ]
['okay', 'ah okay', 'okay', 'ah okay', '0 okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "", "Yeah, yeah.", "Yes, yes." ]
['yeah', 'yes', '', 'yeah yeah', 'yes yes']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but you know what i figured out i am quicker down loading over broad band into my computer than using this hard disk
[ "But you know what I figured out? I'm quicker downloading over broadband into my computer than using this hard disk.", "But you know what I figured out, I'm quicker downloading over broadband into my computer than using this hard disk.", "But you know what I figured out I'm quicker downloading over broadband into my computer than using this hard disk.", "But you know what I figured out. I'm quicker downloading over broadband into my computer than using this hard disk.", "But you know what I figured out? I'm quicker downloading over broadband to my computer than using this hard disk." ]
['but you know what i figured out i am quicker downloading over broadband into my computer than using this hard disk', 'but you know what i figured out i am quicker downloading over broadband into my computer than using this hard disk', 'but you know what i figured out i am quicker downloading over broadband into my computer than using this hard disk', 'but you know what i figured out i am quicker downloading over broadband into my computer than using this hard disk', 'but you know what i figured out i am quicker downloading over broadband to my computer than using this hard disk']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
are they big enough to get anything meaningful out of
[ "Are they big enough to get anything meaningful out of?", "Are they big enough to get anything meaningful out of.", "Are they big enough to get anything meaningful out of it?", "Are they big enough to get anything meaningful out?", "are they big enough to get anything meaningful out of?" ]
['are they big enough to get anything meaningful out of', 'are they big enough to get anything meaningful out of', 'are they big enough to get anything meaningful out of it', 'are they big enough to get anything meaningful out', 'are they big enough to get anything meaningful out of']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i do not think it is very difficult though
[ "I don't think it's very difficult though.", "I think it's very difficult though.", "I don't think it's very difficult, though.", "I think it's very difficult, though.", "I don't think it's very difficult though, you." ]
['i do not think it is very difficult though', 'i think it is very difficult though', 'i do not think it is very difficult though', 'i think it is very difficult though', 'i do not think it is very difficult though you']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
it is just a case of running the script
[ "It's just a case of running the script.", "Just a case of running the script.", "It's a case of running the script.", "just a case of running the script.", "It's just a case of running the sweat." ]
['it is just a case of running the script', 'just a case of running the script', 'it is a case of running the script', 'just a case of running the script', 'it is just a case of running the sweat']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.