Speech-based gender recognition using linear prediction and mel-frequency cepstral coefficients

Yusnita Mohd Ali, Emilia Noorsal, Nor Fadzilah Mokhtar, Siti Zubaidah Md Saad, Mohd Hanapiah Abdullah, Lim Chee Chin


Gender discrimination and awareness are essentially practiced in social, education, workplace, and economic sectors across the globe. A person manifests this attribute naturally in gait, body gesture, facial, including speech. For that reason, automatic gender recognition (AGR) has become an interesting sub-topic in speech recognition systems that can be found in many speech technology applications. However, retrieving salient gender-related information from a speech signal is a challenging problem since speech contains abundant information apart from gender. The paper intends to compare the performance of human vocal tract-based model i.e., linear prediction coefficients (LPC) and human auditory-based model i.e., Mel-frequency cepstral coefficients (MFCC) which are popularly used in other speech recognition tasks by experimentation of optimal feature parameters and classifier’s parameters. The audio data used in this study was obtained from 93 speakers uttering selected words with different vowels. The two feature vectors were tested using two classification algorithms namely, discriminant analysis (DA) and artificial neural network (ANN). Although the experimental results were promising using both feature parameters, the best overall accuracy rate of 97.07% was recorded using MFCC-ANN techniques with almost equal performance for male and female classes.


Artificial neural network; Discriminant analysis; Gender recognition; Linear prediction coefficients; Mel-frequency cepstral coefficients

Full Text:


DOI: http://doi.org/10.11591/ijeecs.v28.i2.pp753-761


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics