Gender Prediction by Speech Analysis

Speech is one of the important methods of communication for humans. The speech signal itself contain linguistic information that can be used to identify the speaker information such as gender, emotions and many more. There are some problems that involve in detection gender of the speaker. In forensi...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-11, Vol.1372 (1), p.12011
Main Authors: Nazifa, Nur Adlin, Fook, Chong Yen, Chin, Lim Chee, Vijean, Vikneswaran, Kheng, Eng Swee
Format: Article
Language:English
Subjects:
Classifiers
Coding
Crime
Feature extraction
Gender
Graphical user interface
K-nearest neighbors algorithm
Linear prediction
Physics
Police
Signal processing
Speech recognition
Support vector machines
Voice communication
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Summary:Speech is one of the important methods of communication for humans. The speech signal itself contain linguistic information that can be used to identify the speaker information such as gender, emotions and many more. There are some problems that involve in detection gender of the speaker. In forensic analysis, the police need to detect criminal profile from any evidence such as voice from any calls and while in healthcare aspect, some vocal fold pathologies can be bias to a particular gender such as vocal fold cyst can be seen particularly in female patients and the patient will have problem with their voices. Three features are extract from the speech signal which are Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding (LPC) and Linear Prediction Coding Coefficient (LPCC). While for the classification, two classifier are used which are Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). The recognition rate is higher for the combination of MFCC and LPCC compared to other features. SVM classifier had outperformed KNN classifier and obtained highest recognition rate of 97.45%. Lastly a graphical user interface system is develop that will record the voice of the speakers, pre-process the signal, extract MFCC and LPCC and classify it using SVM.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1372/1/012011