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- W2242351375 abstract "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." @default.
- W2242351375 created "2016-06-24" @default.
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- W2242351375 date "2015-10-01" @default.
- W2242351375 modified "2023-09-25" @default.
- W2242351375 title "Two-Stage Gender Identification Using Pitch Frequencies, MFCCs and HMMs" @default.
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- W2242351375 doi "https://doi.org/10.1109/smc.2015.501" @default.
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