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Proceedings Paper

Similarity analysis of voice signals using wavelets with dynamic time warping
Author(s): Rahman Tashakkori; Courtney Bowers
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Paper Abstract

Accurately recognizing speech is a difficult task. Differences in gender, accent, pace, tone, as well as defects in the recording equipment and environmental noise can disturb a voice signal. Speech recognition systems are commonly studied and implemented by companies trying to alleviate problems, such as illness or injury, or to increase overall efficiency. This research uses wavelet analysis with several traditional methods to study similarities among sound signals. Through a series of seven steps, a similarity analysis of some voice signals from the same speaker as well as from different speakers is performed. The efficiency of four different wavelets (Haar, db2, db4 and Discrete Morlet), different correlation methods developed previously or in this research, and two different Dynamic Time Warping methods are studied in this research. Through several experiments, it will be shown that these techniques produce excellent results for signals by the same speaker. Based on the limited number of cases studied in this research, some evidence will be presented that suggests the proposed methods on this research are more effective for recognizing male voice files than those of females.

Paper Details

Date Published: 1 April 2003
PDF: 10 pages
Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); doi: 10.1117/12.488141
Show Author Affiliations
Rahman Tashakkori, Appalachian State Univ. (United States)
Courtney Bowers, Appalachian State Univ. (United States)


Published in SPIE Proceedings Vol. 5102:
Independent Component Analyses, Wavelets, and Neural Networks
Anthony J. Bell; Mladen V. Wickerhauser; Harold H. Szu, Editor(s)

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