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Abstract
Gender (Male/Female) classification plays a primary vital role to develop a robust Automatic Tamil Speech Recognition (ASR) applications due to the diversity in the vocal tract of speakers. Various features including Formants (F1, F2, F3, F4), Zero Crossings, and Mel-Frequency Cepstral Coefficients (MFCCs) etc. have appeared in the literature especially for speech/signal classification/recognition. Recently Dalal et al. have proposed a feature called as Histogram of Oriented Gradients (HOG) for extracting feature from an image for efficient detection/classification of objects. We extend and apply the HOG for spectrogram of speech signal and hence called as Spectral Histogram of Oriented Gradients (SHOGs). The results of Tamil language male/female speaker classification using SHOGs features shows good improvement in the classification rate when compared to other features. The results of combination of various features with SHOGs are also promissing.
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