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International Conference on Speech and Computer

SPECOM 2016: Speech and Computer pp 354–361Cite as

Improving Automatic Speech Recognition Containing Additive Noise Using Deep Denoising Autoencoders of LSTM Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9811))

Abstract

Automatic speech recognition systems (ASR) suffer from performance degradation under noisy conditions. Recent work, using deep neural networks to denoise spectral input features for robust ASR, have proved to be successful. In particular, Long Short-Term Memory (LSTM) autoencoders have outperformed other state of the art denoising systems when applied to the mfcc’s of a speech signal. In this paper we also consider denoising LSTM autoencoders (DLSTMA), but instead use three different DLSTMAs and apply each to the mfcc’s, fundamental frequency, and energy features, respectively. Results are given using several kinds of additive noise at different intensity levels, and show how this collection of DLSTMA’s improves the performance of the ASR in comparison with the LSTM autoencoder.

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Acknowledgements

This work was supported by the SEP and CONACyT under the Program SEP-CONACyT, CB-2012-01, No. 182432, in Mexico, as well as the University of Costa Rica in Costa Rica.

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Authors and Affiliations

  1. University of Costa Rica, San José, Costa Rica

    Marvin Coto-Jiménez, John Goddard-Close & Fabiola Martínez-Licona

  2. Metropolitan Autonomous University, México D.F., Mexico

    Marvin Coto-Jiménez, John Goddard-Close & Fabiola Martínez-Licona

Authors
  1. Marvin Coto-Jiménez
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  2. John Goddard-Close
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  3. Fabiola Martínez-Licona
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Corresponding author

Correspondence to Marvin Coto-Jiménez .

Editor information

Editors and Affiliations

  1. SPIIRAS , Saint-Petersburg, Russia

    Andrey Ronzhin

  2. Moscow State Linguistic University , Moscow, Russia

    Rodmonga Potapova

  3. Budapest University of Technology and Economics, Budapest, Hungary

    Géza Németh

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© 2016 Springer International Publishing Switzerland

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Coto-Jiménez, M., Goddard-Close, J., Martínez-Licona, F. (2016). Improving Automatic Speech Recognition Containing Additive Noise Using Deep Denoising Autoencoders of LSTM Networks. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_42

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  • DOI: https://doi.org/10.1007/978-3-319-43958-7_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43957-0

  • Online ISBN: 978-3-319-43958-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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