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2017 | OriginalPaper | Buchkapitel

Language Identification Using Deep Convolutional Recurrent Neural Networks

verfasst von : Christian Bartz, Tom Herold, Haojin Yang, Christoph Meinel

Erschienen in: Neural Information Processing

Verlag: 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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Vorheriges Kapitel Phonemic Restoration Based on the Movement Continuity of Articulation

Nächstes Kapitel Underdetermined Mixture Matrix Estimation Based on Neural Network and Genetic Algorithm

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Titel: Language Identification Using Deep Convolutional Recurrent Neural Networks
verfasst von: Christian Bartz
Tom Herold
Haojin Yang
Christoph Meinel
Verlag: Springer International Publishing
Buch: Neural Information Processing
Print ISBN: 978-3-319-70135-6

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

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-70136-3_93

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