Speech Signal Feature Extraction Based on Wavelet Transform

• Published in: 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation pp. 179 - 182
• Main Authors: Xiaolan Zhao, Zuguo Wu, Jiren Xu, Keren Wang, Jihai Niu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2011
• Subjects: Feature extraction; gaussian mixture model (GMM); Speech; Speech recognition; speech signal; Vectors; Wavelet analysis; Wavelet packets; wavelet transform
• ISBN: 9781457711527; 1457711524
• DOI: 10.1109/ICBMI.2011.80

Analysis of the voice pronunciation mechanism and performance differences of normal voice in the frequency domain, wavelet transform is used to do signal decomposition, and emphasizing characteristics of voice, with these two characteristic parameters we recognize 242 normal voice using gaussian mixture model (GMM) respectively. Put forward wavelet de-noising, entropy coefficient of decomposition (ECD) as the characteristic vector sets of recognition based on the analysis of the multi-scale. Through wavelet transform for the voice goal signal after wavelet packet decomposition, we take the energy character of frequency band as a feature vector. Experiments show that wavelet transform can improve the frequency characteristics of signal, and compress the dimension of characteristics space, and it has very good classification effect of speech signal.