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Automatic Speech Recognition Based on Neural Networks Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Knowledge Transfer for Utterance Classification in Low-Resource Languages

Authors : Andrei Smirnov, Valentin Mendelev

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

The paper deals with a problem of short text classification in Kazakh. Traditional text classification approaches require labeled data to build accurate classifiers. However the amount of available labeled data is usually very limited due to high cost of labeling or data accessibility issues. We describe a method of constructing a classifier without labeled data in the target language. A convolutional neural network (CNN) is trained on Russian labeled texts and a language vector space transform is used to transfer knowledge from Russian into Kazakh. Classification accuracy is evaluated on a dataset of customer support requests. The presented method demonstrates competitive results compared with an approach that employed a sophisticated automatic translation system.

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previous chapter Investigating Signal Correlation as Continuity Metric in a Syllable Based Unit Selection Synthesis System
next chapter Language Identification Using Time Delay Neural Network D-Vector on Short Utterances
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Metadata
Title
Knowledge Transfer for Utterance Classification in Low-Resource Languages
Authors
Andrei Smirnov
Valentin Mendelev
Copyright Year
2016
Book
Speech and Computer

Print ISBN: 978-3-319-43957-0

Electronic ISBN: 978-3-319-43958-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-43958-7_52

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