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Proceedings Paper

Tone detection using wavelet transforms
Author(s): Glenn A. Shelby; Reza R. Adhami
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Paper Abstract

Information regarding the pitch period of a speech signal is an important parameter in designing a speech recognition system for tone based languages such as Chinese. Chinese speech has four fundamental tone types: the tone for a given word is mainly characterized by its vowel section. The four tone types are distinct and can be recognized by examining the tone contour. Researchers have developed several different techniques for speech pitch detection. One recent technique is an event-based pitch detection scheme utilizing the dyadic wavelet transform (DyWT). This pitch detector compares maxima across wavelet scales to locate the beginning of each pitch period. We extend this pitch detection technique to a set of Chinese tone utterances using both the cubic spline wavelet and the Daubechies 4 wavelet. We investigate the impact of applying two preprocessing techniques, center clipping and half wave rectification, as well as several pitch decision logic methods. The DyWT performance is compared to an autocorrelation based pitch detector. The results show that the DyWT pitch detector compares favorably with the autocorrelation pitch detector, and has better performance for some cases. A variety of pitch decision logic techniques improve the DyWT pitch detector performance over the original method.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2491, Wavelet Applications II, (6 April 1995); doi: 10.1117/12.205424
Show Author Affiliations
Glenn A. Shelby, Nichols Research Corp. (United States)
Reza R. Adhami, Univ. of Alabama in Huntsville (United States)


Published in SPIE Proceedings Vol. 2491:
Wavelet Applications II
Harold H. Szu, Editor(s)

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