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|Title:||Two-Stage Gender Identification Using Pitch Frequencies, MFCCs and HMMs|
|Keywords:||Computer Science;Decision Sciences;Energy;Engineering|
|Citation:||Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. (2016), 2879-2884|
|Abstract:||© 2015 IEEE. 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.|
|Appears in Collections:||Scopus 2016-2017|
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