Speaker characterization using spectral subband energy ratio based on Harmonic plus Noise Model

This paper proposes a feature extraction for speaker characterization by exploring the relationship between the two distinct components of the speech signal, one is harmonics accounting for the periodicity of the signal and the other is modulated noise accounting for the turbulences of the glottal airflow. The harmonic and noise parts of the speech signal are decomposed based on the Harmonic plus Noise Model approach. We estimate the spectral subband energy ratios (SSERs) as the speaker characteristic features, which are expected to reflect the interaction property of the vocal tract and glottal airflow of individual speakers for speaker verification. The speaker verification experiments based on a GMM-UBM system have shown the efficiency of the SSER features, reducing the error equal rate by 27.2% by combining with the conventional MFCC features.

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