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Higher-Level Features in Speaker Recognition

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Speaker Classification I

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4343))

Abstract

Higher-level features based on linguistic or long-range information have attracted significant attention in automatic speaker recognition. This article briefly summarizes approaches to using higher-level features for text-independent speaker verification over the last decade. To clarify how each approach uses higher-level information, features are described in terms of their type, temporal span, and reliance on automatic speech recognition for both feature extraction and feature conditioning. A subsequent analysis of higher-level features in a state-of-the-art system illustrates that (1) a higher-level cepstral system outperforms standard systems, (2) a prosodic system shows excellent performance individually and in combination, (3) other higher-level systems provide further gains, and (4) higher-level systems provide increasing relative gains as training data increases. Implications for the general field of speaker classification are discussed.

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  1. SRI International, Menlo Park, CA, International Computer Science Institute, Berkeley, CA,  

    Elizabeth Shriberg

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  1. Elizabeth Shriberg
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Christian Müller

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Shriberg, E. (2007). Higher-Level Features in Speaker Recognition. In: Müller, C. (eds) Speaker Classification I. Lecture Notes in Computer Science(), vol 4343. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74200-5_14

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  • DOI: https://doi.org/10.1007/978-3-540-74200-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74186-2

  • Online ISBN: 978-3-540-74200-5

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