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Voice Activity Detection Based on Discriminative Weight Training Incorporating a Spectral Flatness Measure

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Abstract

In this paper, we present an approach to incorporate discriminative weight training into a statistical model-based voice activity detection (VAD) method. In our approach, the VAD decision rule is derived from the optimally weighted likelihood ratios (LRs) using a minimum classification error (MCE) method. An adaptive on-line means of selecting two kinds of weights based on a power spectral flatness measure (PSFM) is devised for performance improvement. The proposed approach is compared to conventional schemes under various noise conditions, and shows better performance.

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Authors and Affiliations

  1. School of Electronic Engineering, Inha University, Incheon, 402-751, Korea

    Sang-Ick Kang & Joon-Hyuk Chang

Authors
  1. Sang-Ick Kang
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  2. Joon-Hyuk Chang
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Correspondence to Joon-Hyuk Chang.

Additional information

This work was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute for Information Technology Advancement) (IITA-2008-C1090-0902-0010) and This research was financially supported by the MKE and KOTEF through the Human Resource Training Project for Strategic Technology.

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Kang, SI., Chang, JH. Voice Activity Detection Based on Discriminative Weight Training Incorporating a Spectral Flatness Measure. Circuits Syst Signal Process 29, 183–194 (2010). https://doi.org/10.1007/s00034-009-9141-4

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  • DOI: https://doi.org/10.1007/s00034-009-9141-4

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