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2017 | OriginalPaper | Chapter

Language Identification Using Deep Convolutional Recurrent Neural Networks

Authors : Christian Bartz, Tom Herold, Haojin Yang, Christoph Meinel

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks, such as Automatic Speech Recognition (ASR) systems. Without automatic language detection, speech utterances cannot be parsed correctly and grammar rules cannot be applied, causing subsequent speech recognition steps to fail. We propose a LID system that solves the problem in the image domain, rather than the audio domain. We use a hybrid Convolutional Recurrent Neural Network (CRNN) that operates on spectrogram images of the provided audio snippets. In extensive experiments we show, that our model is applicable to a range of noisy scenarios and can easily be extended to previously unknown languages, while maintaining its classification accuracy. We release our code and a large scale training set for LID systems to the community.

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previous chapter Phonemic Restoration Based on the Movement Continuity of Articulation
next chapter Underdetermined Mixture Matrix Estimation Based on Neural Network and Genetic Algorithm
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Metadata
Title
Language Identification Using Deep Convolutional Recurrent Neural Networks
Authors
Christian Bartz
Tom Herold
Haojin Yang
Christoph Meinel
Copyright Year
2017
Book
Neural Information Processing

Print ISBN: 978-3-319-70135-6

Electronic ISBN: 978-3-319-70136-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-70136-3_93

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