Similarity analysis of voice signals using wavelets with dynamic time warping

Rahman Tashakkori; Courtney Bowers

doi:10.1117/12.488141

1 April 2003 Similarity analysis of voice signals using wavelets with dynamic time warping

Rahman Tashakkori, Courtney Bowers

Proceedings Volume 5102, Independent Component Analyses, Wavelets, and Neural Networks; (2003) https://doi.org/10.1117/12.488141
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Rahman Tashakkori and Courtney Bowers "Similarity analysis of voice signals using wavelets with dynamic time warping", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); https://doi.org/10.1117/12.488141

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