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http://dx.doi.org/10.6109/jkiice.2015.19.12.2792

Performance Improvement of Speaker Recognition Using Enhanced Feature Extraction in Glottal Flow Signals and Multiple Feature Parameter Combination  

Kang, Jihoon (Department of Electronics Engineering/ERI, Gyeongsang National University)
Kim, Youngil (Department of Electronics Engineering/ERI, Gyeongsang National University)
Jeong, Sangbae (Department of Electronics Engineering/ERI, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.19, no.12, 2015 , pp. 2792-2799 More about this Journal
Abstract
In this paper, we utilize source mel-frequency cepstral coefficients (SMFCCs), skewness, and kurtosis extracted in glottal flow signals to improve speaker recognition performance. Generally, because the high band magnitude response of glottal flow signals is somewhat flat, the SMFCCs are extracted using the response below the predefined cutoff frequency. The extracted SMFCC, skewness, and kurtosis are concatenated with conventional feature parameters. Then, dimensional reduction by the principal component analysis (PCA) and the linear discriminat analysis (LDA) is followed to compare performances with conventional systems under equivalent conditions. The proposed recognition system outperformed the conventional system for large scale speaker recognition experiments. Especially, the performance improvement was more noticeable for small Gaussan mixtures.
Keywords
speaker recognition; glottal flow; skewness; kurtosis; PCA; LDA;
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