paper_id stringlengths 15 32 | title stringlengths 22 188 | authors listlengths 1 20 | isca_url stringlengths 66 83 | pdf_url stringlengths 65 82 | doi stringlengths 30 30 | pages stringlengths 3 9 | bibtex large_stringlengths 290 737 | abstract large_stringlengths 411 1.7k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|
dubey19b_interspeech | Hypernasality Severity Detection Using Constant Q Cepstral Coefficients | [
"Akhilesh Kumar Dubey",
"S.R. Mahadeva Prasanna",
"S. Dandapat"
] | https://www.isca-archive.org/interspeech_2019/dubey19b_interspeech.html | https://www.isca-archive.org/interspeech_2019/dubey19b_interspeech.pdf | 10.21437/Interspeech.2019-2151 | 4554-4558 | @inproceedings{dubey19b_interspeech,
title = {{Hypernasality Severity Detection Using Constant Q Cepstral Coefficients}},
author = {Akhilesh Kumar Dubey and S.R. Mahadeva Prasanna and S. Dandapat},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4554--4558},
doi = {10.21437/I... | In this work, detection of hypernasality severity in cleft palate speech
is attempted using constant Q cepstral coefficients (CQCC) feature.
The coupling of nasal tract with the oral tract during the production
of hypernasal speech adds nasal formants and anti-formants in low frequency
region of vowel spectrum mainly a... | null | null |
niu19_interspeech | Automatic Depression Level Detection via ℓ-Norm Pooling | [
"Mingyue Niu",
"Jianhua Tao",
"Bin Liu",
"Cunhang Fan"
] | https://www.isca-archive.org/interspeech_2019/niu19_interspeech.html | https://www.isca-archive.org/interspeech_2019/niu19_interspeech.pdf | 10.21437/Interspeech.2019-1617 | 4559-4563 | @inproceedings{niu19_interspeech,
title = {{Automatic Depression Level Detection via ℓp-Norm Pooling}},
author = {Mingyue Niu and Jianhua Tao and Bin Liu and Cunhang Fan},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4559--4563},
doi = {10.21437/Interspeech.2019-1617},
i... | Related physiological studies have shown that Mel-frequency cepstral
coefficient (MFCC) is a discriminative acoustic feature for depression
detection. This fact has led to some works using MFCCs to identify
individual depression degree. However, they rarely adopt neural network
to capture high-level feature associated ... | null | null |
bn19_interspeech | Comparison of Speech Tasks and Recording Devices for Voice Based Automatic Classification of Healthy Subjects and Patients with Amyotrophic Lateral Sclerosis | [
"Suhas B.N.",
"Deep Patel",
"Nithin Rao",
"Yamini Belur",
"Pradeep Reddy",
"Nalini Atchayaram",
"Ravi Yadav",
"Dipanjan Gope",
"Prasanta Kumar Ghosh"
] | https://www.isca-archive.org/interspeech_2019/bn19_interspeech.html | https://www.isca-archive.org/interspeech_2019/bn19_interspeech.pdf | 10.21437/Interspeech.2019-1285 | 4564-4568 | @inproceedings{bn19_interspeech,
title = {{Comparison of Speech Tasks and Recording Devices for Voice Based Automatic Classification of Healthy Subjects and Patients with Amyotrophic Lateral Sclerosis}},
author = {Suhas B.N. and Deep Patel and Nithin Rao and Yamini Belur and Pradeep Reddy and Nalini Atchayar... | We consider the task of speech based automatic classification of patients
with amyotrophic lateral sclerosis (ALS) and healthy subjects. The
role of different speech tasks and recording devices on classification
accuracy is examined. Sustained phoneme production (PHON), diadochokinetic
task (DDK) and spontaneous speech... | null | null |
wang19r_interspeech | A Modified Algorithm for Multiple Input Spectrogram Inversion | [
"Dongxiao Wang",
"Hirokazu Kameoka",
"Koichi Shinoda"
] | https://www.isca-archive.org/interspeech_2019/wang19r_interspeech.html | https://www.isca-archive.org/interspeech_2019/wang19r_interspeech.pdf | 10.21437/Interspeech.2019-3242 | 4569-4573 | @inproceedings{wang19r_interspeech,
title = {{A Modified Algorithm for Multiple Input Spectrogram Inversion}},
author = {Dongxiao Wang and Hirokazu Kameoka and Koichi Shinoda},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4569--4573},
doi = {10.21437/Interspeech.2019-3242}... | We propose a new algorithm to estimate the phase of speech signal in
the mixture of audio sources under the assumption that the magnitude
spectrum of each source is given. The previous method, multiple input
spectrogram inversion algorithm (MISI), often performs poorly when
the magnitude spectrograms estimated are not ... | null | null |
bahmaninezhad19_interspeech | A Comprehensive Study of Speech Separation: Spectrogram vs Waveform Separation | [
"Fahimeh Bahmaninezhad",
"Jian Wu",
"Rongzhi Gu",
"Shi-Xiong Zhang",
"Yong Xu",
"Meng Yu",
"Dong Yu"
] | https://www.isca-archive.org/interspeech_2019/bahmaninezhad19_interspeech.html | https://www.isca-archive.org/interspeech_2019/bahmaninezhad19_interspeech.pdf | 10.21437/Interspeech.2019-3181 | 4574-4578 | @inproceedings{bahmaninezhad19_interspeech,
title = {{A Comprehensive Study of Speech Separation: Spectrogram vs Waveform Separation}},
author = {Fahimeh Bahmaninezhad and Jian Wu and Rongzhi Gu and Shi-Xiong Zhang and Yong Xu and Meng Yu and Dong Yu},
year = {2019},
booktitle = {{Interspeech 2019}}... | Speech separation has been studied widely for single-channel close-talk
microphone recordings over the past few years; developed solutions
are mostly in frequency-domain. Recently, a raw audio waveform separation
network (TasNet) is introduced for single-channel data, with achieving
high Si-SNR (scale-invariant source-... | 1905.07497 | title_snapshot |
inan19_interspeech | Evaluating Audiovisual Source Separation in the Context of Video Conferencing | [
"Berkay İnan",
"Milos Cernak",
"Helmut Grabner",
"Helena Peic Tukuljac",
"Rodrigo C.G. Pena",
"Benjamin Ricaud"
] | https://www.isca-archive.org/interspeech_2019/inan19_interspeech.html | https://www.isca-archive.org/interspeech_2019/inan19_interspeech.pdf | 10.21437/Interspeech.2019-2671 | 4579-4583 | @inproceedings{inan19_interspeech,
title = {{Evaluating Audiovisual Source Separation in the Context of Video Conferencing}},
author = {Berkay İnan and Milos Cernak and Helmut Grabner and Helena Peic Tukuljac and Rodrigo C.G. Pena and Benjamin Ricaud},
year = {2019},
booktitle = {{Interspeech 2019}}... | Source separation involving mono-channel audio is a challenging problem,
in particular for speech separation where source contributions overlap
both in time and frequency. This task is of high interest for applications
such as video conferencing. Recent progress in machine learning has
shown that the combination of vis... | null | null |
ditter19_interspeech | Influence of Speaker-Specific Parameters on Speech Separation Systems | [
"David Ditter",
"Timo Gerkmann"
] | https://www.isca-archive.org/interspeech_2019/ditter19_interspeech.html | https://www.isca-archive.org/interspeech_2019/ditter19_interspeech.pdf | 10.21437/Interspeech.2019-2459 | 4584-4588 | @inproceedings{ditter19_interspeech,
title = {{Influence of Speaker-Specific Parameters on Speech Separation Systems}},
author = {David Ditter and Timo Gerkmann},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4584--4588},
doi = {10.21437/Interspeech.2019-2459},
issn ... | Recent studies have shown that Deep Learning based single-channel speech
separation systems perform worse for same-gender mixtures than for
different-gender mixtures. In this work, we provide for a more detailed
analysis of the respective impact of the fundamental frequency and
the vocal tract length on the system perf... | null | null |
zegers19_interspeech | CNN-LSTM Models for Multi-Speaker Source Separation Using Bayesian Hyper Parameter Optimization | [
"Jeroen Zegers",
"Hugo Van hamme"
] | https://www.isca-archive.org/interspeech_2019/zegers19_interspeech.html | https://www.isca-archive.org/interspeech_2019/zegers19_interspeech.pdf | 10.21437/Interspeech.2019-2423 | 4589-4593 | @inproceedings{zegers19_interspeech,
title = {{CNN-LSTM Models for Multi-Speaker Source Separation Using Bayesian Hyper Parameter Optimization}},
author = {Jeroen Zegers and Hugo {Van hamme}},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4589--4593},
doi = {10.21437/Inters... | In recent years there have been many deep learning approaches towards
the multi-speaker source separation problem. Most use Long Short-Term
Memory - Recurrent Neural Networks (LSTM-RNN) or Convolutional Neural
Networks (CNN) to model the sequential behavior of speech. In this
paper we propose a novel network for source... | 1912.09254 | title_snapshot |
bear19_interspeech | Towards Joint Sound Scene and Polyphonic Sound Event Recognition | [
"Helen L. Bear",
"Inês Nolasco",
"Emmanouil Benetos"
] | https://www.isca-archive.org/interspeech_2019/bear19_interspeech.html | https://www.isca-archive.org/interspeech_2019/bear19_interspeech.pdf | 10.21437/Interspeech.2019-2169 | 4594-4598 | @inproceedings{bear19_interspeech,
title = {{Towards Joint Sound Scene and Polyphonic Sound Event Recognition}},
author = {Helen L. Bear and Inês Nolasco and Emmanouil Benetos},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4594--4598},
doi = {10.21437/Interspeech.2019-2169... | Acoustic Scene Classification (ASC) and Sound Event Detection (SED)
are two separate tasks in the field of computational sound scene analysis.
In this work, we present a new dataset with both sound scene and sound
event labels and use this to demonstrate a novel method for jointly
classifying sound scenes and recognizi... | 1904.10408 | title_snapshot |
fan19c_interspeech | Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features | [
"Cunhang Fan",
"Bin Liu",
"Jianhua Tao",
"Jiangyan Yi",
"Zhengqi Wen"
] | https://www.isca-archive.org/interspeech_2019/fan19c_interspeech.html | https://www.isca-archive.org/interspeech_2019/fan19c_interspeech.pdf | 10.21437/Interspeech.2019-1940 | 4599-4603 | @inproceedings{fan19c_interspeech,
title = {{Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features}},
author = {Cunhang Fan and Bin Liu and Jianhua Tao and Jiangyan Yi and Zhengqi Wen},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4599--4603},
doi ... | Deep clustering (DC) and utterance-level permutation invariant training
(uPIT) have been demonstrated promising for speaker-independent speech
separation. DC is usually formulated as two-step processes: embedding
learning and embedding clustering, which results in complex separation
pipelines and a huge obstacle in dir... | 1907.09884 | title_snapshot |
yousefi19_interspeech | Probabilistic Permutation Invariant Training for Speech Separation | [
"Midia Yousefi",
"Soheil Khorram",
"John H.L. Hansen"
] | https://www.isca-archive.org/interspeech_2019/yousefi19_interspeech.html | https://www.isca-archive.org/interspeech_2019/yousefi19_interspeech.pdf | 10.21437/Interspeech.2019-1827 | 4604-4608 | @inproceedings{yousefi19_interspeech,
title = {{Probabilistic Permutation Invariant Training for Speech Separation}},
author = {Midia Yousefi and Soheil Khorram and John H.L. Hansen},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4604--4608},
doi = {10.21437/Interspeech.201... | Single-microphone, speaker-independent speech separation is normally
performed through two steps: (i) separating the specific speech sources,
and (ii) determining the best output-label assignment to find the
separation error. The second step is the main obstacle in training
neural networks for speech separation. Rece... | 1908.01768 | title_snapshot |
shi19e_interspeech | Which Ones Are Speaking? Speaker-Inferred Model for Multi-Talker Speech Separation | [
"Jing Shi",
"Jiaming Xu",
"Bo Xu"
] | https://www.isca-archive.org/interspeech_2019/shi19e_interspeech.html | https://www.isca-archive.org/interspeech_2019/shi19e_interspeech.pdf | 10.21437/Interspeech.2019-1591 | 4609-4613 | @inproceedings{shi19e_interspeech,
title = {{Which Ones Are Speaking? Speaker-Inferred Model for Multi-Talker Speech Separation}},
author = {Jing Shi and Jiaming Xu and Bo Xu},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4609--4613},
doi = {10.21437/Interspeech.2019-1591}... | Recent deep learning methods have gained noteworthy success in the
multi-talker mixed speech separation task, which is also famous known
as the Cocktail Party Problem. However, most existing models are well-designed
towards some predefined conditions, which make them unable to handle
the complex auditory scene automati... | null | null |
shi19f_interspeech | End-to-End Monaural Speech Separation with Multi-Scale Dynamic Weighted Gated Dilated Convolutional Pyramid Network | [
"Ziqiang Shi",
"Huibin Lin",
"Liu Liu",
"Rujie Liu",
"Shoji Hayakawa",
"Shouji Harada",
"Jiqing Han"
] | https://www.isca-archive.org/interspeech_2019/shi19f_interspeech.html | https://www.isca-archive.org/interspeech_2019/shi19f_interspeech.pdf | 10.21437/Interspeech.2019-1292 | 4614-4618 | @inproceedings{shi19f_interspeech,
title = {{End-to-End Monaural Speech Separation with Multi-Scale Dynamic Weighted Gated Dilated Convolutional Pyramid Network}},
author = {Ziqiang Shi and Huibin Lin and Liu Liu and Rujie Liu and Shoji Hayakawa and Shouji Harada and Jiqing Han},
year = {2019},
book... | The monaural speech separation technology is far from satisfactory
and has been a challenging task due to the interference of multiple
sound sources. While deep dilated temporal convolutional networks (TCN)
have been proved to be very effective in sequence modeling, this work
investigates how to extend TCN to result in... | 1902.04891 | title_judge |
lluis19_interspeech | End-to-End Music Source Separation: Is it Possible in the Waveform Domain? | [
"Francesc Lluís",
"Jordi Pons",
"Xavier Serra"
] | https://www.isca-archive.org/interspeech_2019/lluis19_interspeech.html | https://www.isca-archive.org/interspeech_2019/lluis19_interspeech.pdf | 10.21437/Interspeech.2019-1177 | 4619-4623 | @inproceedings{lluis19_interspeech,
title = {{End-to-End Music Source Separation: Is it Possible in the Waveform Domain?}},
author = {Francesc Lluís and Jordi Pons and Xavier Serra},
year = {2019},
booktitle = {{Interspeech 2019}},
pages = {4619--4623},
doi = {10.21437/Interspeech.2019... | Most of the currently successful source separation techniques use the
magnitude spectrogram as input, and are therefore by default omitting
part of the signal: the phase. To avoid omitting potentially useful
information, we study the viability of using end-to-end models for
music source separation — which take into acc... | 1810.12187 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.