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Noisy Speech Endpoint Detection using Robust Feature

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Biometric Authentication (BIOMET 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8897))

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Abstract.

In this paper a new robust feature for speech endpoint detection is proposed. It combines the properties of the Modified Group Delay Spectrum (MGDS) and the Mean Delta (MD) approach in order to obtain the more robust endpoint detection. This feature is named as Group Delay Mean Delta (GDMD) feature. The effectiveness of proposed feature and other three features for trajectory-based endpoint detection is experimentally evaluated in the fixed-text Dynamic Time Warping (DTW) - based speaker verification task with short phrases of telephone speech. The analysed features are - Modified Teager Energy (MTE), Energy-Entropy (EE) feature and MD feature. The results of the experiments have shown that the GDMD feature demonstrates the best performance in endpoint detection tests in terms of verification rate.

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Author information

Authors and Affiliations

  1. Institute of Information and Communication Technologies, Sofia, Bulgaria

    Atanas Ouzounov

Authors
  1. Atanas Ouzounov
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Correspondence to Atanas Ouzounov .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Virginio Cantoni

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Dimo Dimov

  3. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

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© 2014 Springer International Publishing Switzerland

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Ouzounov, A. (2014). Noisy Speech Endpoint Detection using Robust Feature. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-13386-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13385-0

  • Online ISBN: 978-3-319-13386-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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