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Pitch-based Gender Identification with Two-stage Classification

By Yakun Hu, Dapeng Wu and Antonio Nucci


In this paper, we address the speech-based gender identification problem. Mel-Frequency Cepstral Coefficients (MFCC) of voice samples are typically used as the features for gender identification. However, MFCC-based classification incurs high complexity. This paper proposes a novel pitch-based gender identification system with a two-stage classifier to ensure accurate identification and low complexity. The first stage of the classifier identifies and labels all the speakers whose pitch clearly indicates the gender of the speaker; the complexity of this stage is very low since only threshold-based decision rule on a scalar (i.e., pitch) is used. The ambiguous voice samples from all the other speakers (which cannot be classified with high accuracy by the first stage, and can be regarded as suspicious speakers or difficult cases) are forwarded to the second-stage for finer examination; the second-stage of our classifier uses Gaussian Mixture Model (GMM) to accurately isolate voice samples based on gender. Experiment results show that our system is speech language/content independent, microphone independent, and robust against noisy recording conditions. Our system is extremely accurate with probability of correct classification of 98.65%, and very efficient with about 5 seconds required for feature extraction and classification

Topics: Index Terms Gender Identification, Pitch, Energy Separation, Suspicious Speaker Detection, Gaussian Mixture Model (GMM
Year: 2011
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