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In recent years wavelet packet (WP) transform has been used as an important speech representation tool. WP based acoustic features have found to be more effective than the short time Fourier transform (STFT) based features to capture the information of unvoiced phoneme in continuous speech. But wavelet features fail to carry the same usefulness to represent the voiced phonemes such as vowels, nasals. This paper proposes a new WP sub-band based features by taking care of harmonic information of voiced speech signal. It has been noticed that most of the voiced energy of the speech signal lies in between 250Hz-2000Hz. Thus the proposed technique emphasizes the individual sub-band harmonic energy upto 2 kHz. The speech signal is decomposed into 16 wavelet sub-bands and Harmonic energy features (HEF) are combined with wavelet packet cepstral features (WPCC). More in IET Signal Processing Digital Library

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A gender classification system is proposed based on pitch, formants and combination of both. Ten Hindi digits database has been prepared for fifty speakers. Each Speaker has spoken each digit ten times. Formants derived from speech samples have been used for gender classification. Gender classification has been also done by using pitch extracted from different methods. Autocorrelation, Cepstrum and Average Magnitude Difference (AMDF) methods have been used for pitch determination from speech samples. Formants in combination with pitch are also used for gender classification. A feature vector consisting of pitches derived from all the above mentioned pitch determination methods was also used for gender classification. Experiments were performed for both open-set and closed-set gender classification. Autocorrelation method performed best for gender classification in open-set. Hybrid method (Autocorrelation +AMDF+ Cepstrum) performed best for gender classification in closed-set.

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