Two-Stage Gender Identification Using Pitch Frequencies, MFCCs and HMMs

This paper proposes a two-stage method, which can identify the gender of a speaker from spoken syllables that have different tones, using: an average pitch frequency, MFCC-based features (Mel-Frequency Cepstral Coefficients), and Hidden Markov Models (HMM). The method can be divided into 2 stages. At the first stage, an average pitch frequency of each speaker was used to classify the gender. Still, a number of ambiguous speakers who were not clearly classified at the first stage were then forwarded to the second stage. At the second stage, gender identification using: MFCC features, phoneme acoustic models for females and males, and grammar for gender recognition was applied. The experimental results show that the proposed method achieved a high correct gender identification rate of 98.92%, which was higher than the conventional method using an average pitch frequency and a threshold. The proposed method is also more accurate than the Artificial Neural Network (ANN) with pitch frequencies. The results indicate that the proposed method is a practical and efficient way to identify gender.