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Improved Data Modeling for Text-Dependent Speaker Recognition Using Sub-Band Processing

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Abstract

A growing body of recent work documents the potential benefits of sub-band processing over wideband processing in automatic speech recognition and, less usually, speaker recognition. It is often found that the sub-band approach delivers performance improvements (especially in the presence of noise), but not always so. This raises the question of precisely when and how sub-band processing might be advantageous, which is difficult to answer because there is as yet only a rudimentary theoretical framework guiding this work. We describe a simple sub-band speaker recognition system designed to facilitate experimentation aimed at increasing understanding of the approach. This splits the time-domain speech signal into 16 sub-bands using a bank of second-order filters spaced on the psychophysical mel scale. Each sub-band has its own separate cepstral-based recognition system, the outputs of which are combined using the sum rule to produce a final decision. We find that sub-band processing leads to worthwhile reductions in both the verification and identification error rates relative to the wideband system, decreasing the identification error rate from 3.33% to 0.56% and equal error rate for verification by approximately 50% for clean speech. The hypothesis is advanced that, unlike the wideband system, sub-band processing effectively constrains the free parameters of the speaker models to be more uniformly deployed across frequency: as such, it offers a practical solution to the bias/variance dilemma of data modeling. Much remains to be done to explore fully the new paradigm of sub-band processing. Accordingly, several avenues for future work are identified. In particular, we aim to explore the hypothesis of a practical solution to the bias/variance dilemma in more depth.

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Authors and Affiliations

  1. School of Engineering, University of Abertay Dundee, Scotland, DD1 1HG

    R.A. Finan & A.T. Sapeluk

  2. Image, Speech and Intelligent Systems (ISIS) Research Group, Department of Electronics and Computer Science, University of Southampton, Hants, SO17 1BJ, UK

    R.I. Damper

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  1. R.A. Finan
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  2. R.I. Damper
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  3. A.T. Sapeluk
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Finan, R., Damper, R. & Sapeluk, A. Improved Data Modeling for Text-Dependent Speaker Recognition Using Sub-Band Processing. International Journal of Speech Technology 4, 45–62 (2001). https://doi.org/10.1023/A:1009652732313

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