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2021 | OriginalPaper | Chapter

From Bottom to Top: A Coordinated Feature Representation Method for Speech Recognition

Authors : Lixia Zhou, Jun Zhang

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

This article introduces a novel coordinated representation method, termed MFCC aided sparse representation (MSR), for speech recognition. The proposed MSR combines a top level sparse representation feature with the conventional MFCC, i.e., a bottom level feature of speech, so that complex information of various hidden attributes in the speech can be contained. A neural network architecture with attention mechanism has also been designed to validate the effective of the proposed MSR for speech recognition. Experiments on the TIMIT database show that significant performance improvements, in terms of recognition accuracy, can be obtained by the proposed MSR compared with the scenarios that adopt the MFCC or the sparse representation solely.

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Title: From Bottom to Top: A Coordinated Feature Representation Method for Speech Recognition
Authors: Lixia Zhou
Jun Zhang
Publisher: Springer International Publishing
Book: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68779-3

Electronic ISBN: 978-3-030-68780-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-68780-9_33

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