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International Conference on Pattern Recognition

ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges pp 396–403Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12666))

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.

This work was supported in part by the NSFC under Grant 61973088, and in part by the NSF of Guangdong under Grant 2019A1515011371.

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Authors and Affiliations

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China

    Lixia Zhou & Jun Zhang

Authors
  1. Lixia Zhou
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  2. Jun Zhang
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Corresponding author

Correspondence to Jun Zhang .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Zhou, L., Zhang, J. (2021). From Bottom to Top: A Coordinated Feature Representation Method for Speech Recognition. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_33

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  • DOI: https://doi.org/10.1007/978-3-030-68780-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68779-3

  • Online ISBN: 978-3-030-68780-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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