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Audio Visual Speech Recognition Using Deep Recurrent Neural Networks

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Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10183))

Abstract

In this work, we propose a training algorithm for an audio-visual automatic speech recognition (AV-ASR) system using deep recurrent neural network (RNN). First, we train a deep RNN acoustic model with a Connectionist Temporal Classification (CTC) objective function. The frame labels obtained from the acoustic model are then used to perform a non-linear dimensionality reduction of the visual features using a deep bottleneck network. Audio and visual features are fused and used to train a fusion RNN. The use of bottleneck features for visual modality helps the model to converge properly during training. Our system is evaluated on GRID corpus. Our results show that presence of visual modality gives significant improvement in character error rate (CER) at various levels of noise even when the model is trained without noisy data. We also provide a comparison of two fusion methods: feature fusion and decision fusion.

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Authors and Affiliations

  1. Samsung R&D Institute India, Bangalore, Bangalore, India

    Abhinav Thanda & Shankar M. Venkatesan

Authors
  1. Abhinav Thanda
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  2. Shankar M. Venkatesan
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Corresponding author

Correspondence to Abhinav Thanda .

Editor information

Editors and Affiliations

  1. Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

  2. Multimodal Communication and Computation, University of Southern California, Playa Vista, California, USA

    Stefan Scherer

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Thanda, A., Venkatesan, S.M. (2017). Audio Visual Speech Recognition Using Deep Recurrent Neural Networks. In: Schwenker, F., Scherer, S. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2016. Lecture Notes in Computer Science(), vol 10183. Springer, Cham. https://doi.org/10.1007/978-3-319-59259-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-59259-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59258-9

  • Online ISBN: 978-3-319-59259-6

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