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Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognition

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New Era for Robust Speech Recognition

Abstract

Deep neural networks (DNNs) have been successfully applied to many pattern classification problems, including acoustic modelling for automatic speech recognition (ASR). However, DNN adaptation remains a challenging task. Many approaches have been proposed in recent years to improve the adaptability of DNNs to achieve robust ASR. This chapter will review the recent adaptation methods for DNNs, broadly categorising them into constrained adaptation, feature normalisation, feature augmentation and structured DNN parameterisation. Specifically, we will describe various methods of estimating reliable representations for feature augmentation, focusing primarily on comparing i-vectors and other bottleneck features. Moreover, we will also present an adaptable DNN layer parameterisation scheme based on a linear interpolation structure. The interpolation weights can be reliably adjusted to adapt the DNN to different conditions. This generic scheme subsumes many existing DNN adaptation methods, including speaker-code adaptation, learning hidden unit contribution factorised hidden layer and cluster adaptive training for DNNs.

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Notes

  1. 1.

    A speaker super-vector is a concatenation of the mean vectors of a Gaussian mixture model that represents the feature distribution for each speaker.

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Authors and Affiliations

  1. National University of Singapore, Lower Kent Ridge Road, Singapore, 119077, Singapore

    Khe Chai Sim, Gautam Mantena, Lahiru Samarakoon & Souvik Kundu

  2. Shanghai Jiao Tong University, Shanghai, China

    Yanmin Qian & Tian Tan

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  1. Khe Chai Sim
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  2. Yanmin Qian
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  3. Gautam Mantena
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Correspondence to Khe Chai Sim .

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  1. Mitsubishi Electric Research Laboratories (MERL), Cambridge, Massachusetts, USA

    Shinji Watanabe

  2. NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan

    Marc Delcroix

  3. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Florian Metze

  4. Mitsubishi Electric Research Laboratories (MERL), Cambridge, Massachusetts, USA

    John R. Hershey

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Sim, K.C., Qian, Y., Mantena, G., Samarakoon, L., Kundu, S., Tan, T. (2017). Adaptation of Deep Neural Network Acoustic Models for Robust Automatic Speech Recognition. In: Watanabe, S., Delcroix, M., Metze, F., Hershey, J. (eds) New Era for Robust Speech Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-64680-0_9

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