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Robust Voice Activity Detection with Deep Maxout Neural Networks

Author

Listed:
  • Valentin Sergeyevich Mendelev
  • Tatiana Nikolaevna Prisyach
  • Alexey Alexandrovich Prudnikov

Abstract

Voice activity detection (VAD) under non-stationary noises is a very important task to solve when using a real-life system of automatic speech recognition, especially if a remote microphone is used. Many existing methods do not work well with noise that changes over time or with very low signal-to-noise ratio (SNR). This paper proposes a method based on deep maxout neural networks with dropout regularization. The method is effective even for very low SNR (up to -5dB). The robustness of the method is demonstrated by low FR/FA error rates on a test dataset that was recorded under conditions different from the training dataset.

Suggested Citation

  • Valentin Sergeyevich Mendelev & Tatiana Nikolaevna Prisyach & Alexey Alexandrovich Prudnikov, 2015. "Robust Voice Activity Detection with Deep Maxout Neural Networks," Modern Applied Science, Canadian Center of Science and Education, vol. 9(8), pages 153-153, August.
    • Handle: RePEc:ibn:masjnl:v:9:y:2015:i:8:p:153
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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