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Prosodic Features for Speaker Recognition

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Forensic Speaker Recognition

Abstract

In this chapter the effectiveness of syllable-based prosodic features for speaker recognition is discussed. The term prosody represents a collection of characteristics such as intonation, stress and timing, primarily expressed using variations in pitch, energy and duration at various levels of speech. Prosody reflects the learned/acquired speaking habits of a person and hence contributes for speaker recognition. Because prosodic features are less affected by channel mismatch and noise, they are particularly well suited for speaker forensics, a field that demands accurate identification of suspects with as few mitigating conditions as possible. In this chapter, the author describes a method for extracting prosodic features directly from speech signal. Applying this method, speech is segmented into syllable-like regions using vowel onset points (VOP). The locations of VOPs serve as reference for extraction and representation of prosodic features. The effectiveness of the prosodic features for speaker recognition is demonstrated for extended task of NIST speaker recognition evaluation 2003. Combining evidence from spectral features with that of the proposed prosodic features helps to improve overall speaker recognition accuracy.

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Acknowledgement

The author would like to thank Prof. B. Yegnanarayana and members of Speech and Vision Laboratory of IIT Madras, India during 2002–2006 for their support to carry out the study described in this chapter.

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  1. Rajiv Gandhi Institute of Technology, 686501, Kottayam, Kerala, India

    Leena Mary Ph.D.

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  1. Leena Mary Ph.D.
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Correspondence to Leena Mary Ph.D. .

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  1. Lingustic Technology Systems, Palisade Ave Apt 1809 800, Fort Lee, 07024-4121, New Jersey, USA

    Amy Neustein

  2. Near Indroda Circle, DA-IICT, Room 4103, Faculty Block 4, Gandhinagar, 382 007, Gujarat, India

    Hemant A. Patil

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Mary, L. (2012). Prosodic Features for Speaker Recognition. In: Neustein, A., Patil, H. (eds) Forensic Speaker Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0263-3_13

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  • DOI: https://doi.org/10.1007/978-1-4614-0263-3_13

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