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Wavelet Based Noise Robust Features for Speaker Recognition
Vibha Tiwari, Jyoti Singhai
Pages - 52 - 64 | Revised - 01-05-2011 | Published - 31-05-2011
Published in Signal Processing: An International Journal (SPIJ)
MORE INFORMATION
KEYWORDS
Speaker Recognition, Mel Frequency Cepstral Coefficients (MFCC), Amplitude Modulation (AM), Wavelet Filterbank.
ABSTRACT
Extraction and selection of the best parametric representation of acoustic signal is the most important task in designing any speaker recognition system. A wide range of possibilities exists for parametrically representing the speech signal such as Linear Prediction Coding (LPC) ,Mel frequency Cepstrum coefficients (MFCC) and others. MFCC are currently the most popular choice for any speaker recognition system, though one of the shortcomings of MFCC is that the signal is assumed to be stationary within the given time frame and is therefore unable to analyze the non-stationary signal. Therefore it is not suitable for noisy speech signals. To overcome this problem several researchers used different types of AM-FM modulation/demodulation techniques for extracting features from speech signal. In some approaches it is proposed to use the wavelet filterbanks for extracting the features. In this paper a technique for extracting the features by combining the above mentioned approaches is proposed. Features are extracted from the envelope of the signal and then passed through wavelet filterbank. It is found that the proposed method outperforms the existing feature extraction techniques.
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Mr. Vibha Tiwari
Gyan Ganga Institute of Technology and management Bhopal, India - India
vibhatiwari19@gmail.com
Dr. Jyoti Singhai
Maulana Azad National Institute Of Technology Bhopal, India - India
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