Spectral amplitude nonlinearities for improved noise robustness of spectral features for use in automatic speech recognition

Stephen Zahorian; Brian Wong

doi:10.1121/1.3655077

Spectral amplitude nonlinearities for improved noise robustness of spectral features for use in automatic speech recognition

The Journal of the Acoustical Society of America ◽

10.1121/1.3655077 ◽

2011 ◽

Vol 130 (4) ◽

pp. 2524-2524

Author(s):

Stephen Zahorian ◽

Brian Wong

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Noise Robustness ◽

Spectral Amplitude ◽

Spectral Features

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Combining De-noising Auto-encoder and Recurrent Neural Networks in End-to-End Automatic Speech Recognition for Noise Robustness

2018 IEEE Spoken Language Technology Workshop (SLT) ◽

10.1109/slt.2018.8639597 ◽

2018 ◽

Author(s):

Tzu-Hsuan Ting ◽

Chia-Ping Chen

Keyword(s):

Neural Networks ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Recurrent Neural Networks ◽

Noise Robustness ◽

End To End

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Improving Noise Robustness of Automatic Speech Recognition via Parallel Data and Teacher-student Learning

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2019.8683422 ◽

2019 ◽

Cited By ~ 8

Author(s):

Ladislav Mosner ◽

Minhua Wu ◽

Anirudh Raju ◽

Sree Hari Krishnan Parthasarathi ◽

Kenichi Kumatani ◽

...

Keyword(s):

Speech Recognition ◽

Student Learning ◽

Automatic Speech Recognition ◽

Noise Robustness ◽

Teacher Student ◽

Parallel Data

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Robust automatic speech recognition using an optimal spectral amplitude estimator algorithm in low-SNR car environments

10.21437/interspeech.2004-624 ◽

2004 ◽

Author(s):

Zili Li ◽

Hesham Tolba ◽

Douglas O'Shaughnessy

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Spectral Amplitude ◽

Low Snr

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Controlling the Noise Robustness of End-to-End Automatic Speech Recognition Systems

10.1109/ijcnn52387.2021.9533390 ◽

2021 ◽

Author(s):

Matthias Moller ◽

Johannes Twiefel ◽

Cornelius Weber ◽

Stefan Wermter

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Noise Robustness ◽

Recognition Systems ◽

End To End

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Parametric forms of spectral amplitude nonlinearities for use in automatic speech recognition

The Journal of the Acoustical Society of America ◽

10.1121/1.4755670 ◽

2012 ◽

Vol 132 (3) ◽

pp. 2079-2079

Author(s):

Stephen Zahorian

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Spectral Amplitude

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Techniques for Noise Robustness in Automatic Speech Recognition

10.1002/9781118392683 ◽

2012 ◽

Cited By ~ 52

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Noise Robustness

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Robust automatic speech recognition using a perceptually-based optimal spectral amplitude estimator speech enhancement algorithm in various low-SNR environments

10.21437/interspeech.2005-223 ◽

2005 ◽

Author(s):

Hesham Tolba ◽

Zili Li ◽

Douglas O'Shaughnessy

Keyword(s):

Speech Recognition ◽

Speech Enhancement ◽

Automatic Speech Recognition ◽

Spectral Amplitude ◽

Low Snr ◽

Enhancement Algorithm

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A curriculum learning method for improved noise robustness in automatic speech recognition

2017 25th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco.2017.8081267 ◽

2017 ◽

Cited By ~ 7

Author(s):

Stefan Braun ◽

Daniel Neil ◽

Shih-Chii Liu

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Noise Robustness ◽

Learning Method

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Automatic speech recognition at the University of Paris

PsycEXTRA Dataset ◽

10.1037/e506252009-002 ◽

1975 ◽

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

University Of Paris ◽

The University

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Selective Adaptation of End-to-End Speech Recognition using Hybrid CTC/Attention Architecture for Noise Robustness

2020 28th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco47968.2020.9287836 ◽

2021 ◽

Author(s):

Cong-Thanh Do ◽

Shucong Zhang ◽

Thomas Hain

Keyword(s):

Speech Recognition ◽

Selective Adaptation ◽

Noise Robustness ◽

End To End

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