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Statistical Analysis and Evaluation of Blind Speech Extraction Algorithms

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Blind Source Separation

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

In this chapter, a problem of blind source separation for speech applications operated under real acoustic environments is addressed. In particular, we focus on a blind spatial subtraction array (BSSA) consisting of a noise estimator based on independent component analysis (ICA) for efficient speech enhancement. First, it is theoretically and experimentally pointed out that ICA is proficient in noise estimation rather than in speech estimation under a nonpoint-source noise condition. Next, motivated by the above-mentioned fact, we introduce a structure-generalized parametric BSSA, which consists of an ICA-based noise estimator and post-filtering based on generalized spectral subtraction. In addition, we perform its theoretical analysis via higher-order statistics. Comparing a parametric BSSA and a parametric channelwise BSSA, we reveal that a channelwise BSSA structure is recommended for listening but a conventional BSSA is more suitable for speech recognition.

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Authors and Affiliations

  1. The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Hiroshi Saruwatari

  2. Nara Institute of Science and Technology, Nara, 630-0192, Japan

    Ryoichi Miyazaki

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  1. Hiroshi Saruwatari
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  2. Ryoichi Miyazaki
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Correspondence to Hiroshi Saruwatari .

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Editors and Affiliations

  1. University of Technology, Sydney, Sydney, Australia

    Ganesh R. Naik

  2. University of Surrey, Guildford, United Kingdom

    Wenwu Wang

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Saruwatari, H., Miyazaki, R. (2014). Statistical Analysis and Evaluation of Blind Speech Extraction Algorithms. In: Naik, G., Wang, W. (eds) Blind Source Separation. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55016-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-55016-4_10

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