Effects of Phoneme Type and Frequency on Distributed Speaker Identification and Verification

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Abstract

In the European Telecommunication Standards Institute (ETSI), Distributed Speech Recognition (DSR) front-end, the distortion added due to feature compression on the front end side increases the variance flooring effect, which in turn increases the identification error rate. The penalty incurred in reducing the bit rate is the degradation in speaker recognition performance. In this paper, we present a nontraditional solution for the previously mentioned problem. To reduce the bit rate, a speech signal is segmented at the client, and the most effective phonemes (determined according to their type and frequency) for speaker recognition are selected and sent to the server. Speaker recognition occurs at the server. Applying this approach to YOHO corpus, we achieved an identification error rate (ER) of 0.05% using an average segment of 20.4% for a testing utterance in a speaker identification task. We also achieved an equal error rate (EER) of 0.42% using an average segment of 15.1% for a testing utterance in a speaker verification task.

Journal

  • IEICE Trans. Inf & Syst., D

    IEICE Trans. Inf & Syst., D 89 (5), 1712-1719, 2006-05-01

    The Institute of Electronics, Information and Communication Engineers

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Keywords

Details 詳細情報について

  • CRID
    1573387452395837184
  • NII Article ID
    110007503083
  • NII Book ID
    AA10826272
  • ISSN
    09168532
  • Text Lang
    en
  • Data Source
    • CiNii Articles

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