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Data Augmentation for Training of Noise Robust Acoustic Models

  • Tatiana Prisyach
  • Valentin Mendelev
  • Dmitry Ubskiy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)

Abstract

In this paper we analyse ways to improve the acoustic models based on deep neural networks with the help of data augmentation. These models are used for speech recognition in a priori unknown possibly noisy acoustic environment (with the presence of office or home noise, street noise, babble, etc.) and may deal with both the headset and distant microphone recordings. We compare acoustic models trained on speech corpora with artificially added noises of different origins and reverberation. At various test sets, word recognition accuracy improvement over the baseline model trained on clean headset recordings reaches 45%. In real-life environments like a meeting room or a noisy open space, the gain varies from 10 to 40%.

Keywords

Data augmentation Robust speech recognition Deep neural network 

Notes

Acknowledgements

This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.579.21.0057 (ID RFMEFI57914X0057).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Tatiana Prisyach
    • 1
    • 2
  • Valentin Mendelev
    • 2
  • Dmitry Ubskiy
    • 3
  1. 1.STC-Innovations Ltd.St. PetersburgRussia
  2. 2.Speech Technology CenterSt. PetersburgRussia
  3. 3.ITMO-UniversitySt. PetersburgRussia

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