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Automatic Speech Recognition Based on Neural Networks

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

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Abstract

In automatic speech recognition, as in many areas of machine learning, stochastic modeling relies on neural networks more and more. Both in acoustic and language modeling, neural networks today mark the state of the art for large vocabulary continuous speech recognition, providing huge improvements over former approaches that were solely based on Gaussian mixture hidden markov models and count-based language models. We give an overview of current activities in neural network based modeling for automatic speech recognition. This includes discussions of network topologies and cell types, training and optimization, choice of input features, adaptation and normalization, multitask training, as well as neural network based language modeling. Despite the clear progress obtained with neural network modeling in speech recognition, a lot is to be done, yet to obtain a consistent and self-contained neural network based modeling approach that ties in with the former state of the art. We will conclude by a discussion of open problems as well as potential future directions w.r.t. to neural network integration into automatic speech recognition systems.

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  1. Lehrstuhl Informatik 6, RWTH Aachen University, 52074, Aachen, Germany

    Ralf Schlüter, Patrick Doetsch, Pavel Golik, Markus Kitza, Tobias Menne, Kazuki Irie, Zoltán Tüske & Albert Zeyer

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  1. Ralf Schlüter
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  2. Patrick Doetsch
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Correspondence to Ralf Schlüter .

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  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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Schlüter, R. et al. (2016). Automatic Speech Recognition Based on Neural 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_1

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