Gender Identification using Spectral Features and Glottal Closure Instants (GCIs)

• Published in: 2019 Twelfth International Conference on Contemporary Computing (IC3) pp. 1 - 6
• Main Authors: Ramteke, Pravin Bhaskar, Supanekar, Sujata, Koolagudi, Shashidhar G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2019
• Subjects: Feature extraction; Gender Identification; Glottal Closure Instants (GCIs); Mel frequency cepstral coefficient; Pitch; Radio frequency; Random Forest; Speech recognition; Support vector machines; Task analysis
• ISSN: 2572-6129
• DOI: 10.1109/IC3.2019.8844908

Automatic identification of gender from speech may help to improve the performance of the systems such as speaker & speech recognition, forensic analysis, authentication processes. The difference in the physiological parameters of male and female vocal folds results in significant changes in their vocal fold vibration pattern. These changes can be characterized from the differences in the duration of their glottal closure. In this paper, an attempt has been made for gender recognition from speech using spectral features such as MFCCs, LPCCs, etc.; pitch (F0), excitation source features like glottal closure instants (GCIs) and its statistical variations. Western Michigan University's Gender dataset is used for experimentation. The dataset is collected from 93 speakers consisting of speech from 45 male and 48 female speakers respectively. Random forests (RFs) and Support vector machines (SVMs) are used to measure the performance of the proposed features. Random forest is observed to achieve average frame level accuracy of 96.908% using 13 MFCCs, 13 LPCCs, Pitch (F0) and GCI Stats (5). SVM is observed to achieve an average accuracy of 98.607% using 13 MFCCs, 13 LPCCs and GCI Stats (5). From the results, it is observed that the proposed features are efficient in discriminating the gender from speech.