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

Glottal flow 신호에서의 향상된 특징추출 및 다중 특징파라미터 결합을 통한 화자인식 성능 향상

Kang, Jihoon;Kim, Youngil;Jeong, Sangbae

  • Received : 2015.10.22
  • Accepted : 2015.11.23
  • Published : 2015.12.31


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.


speaker recognition;glottal flow;skewness;kurtosis;PCA;LDA


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Supported by : National Research Foundation of Korea(NRF)