Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Voice-based gender identification in multimedia applications
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Foreground auditory scene analysis for hearing aids
Pattern Recognition Letters
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Gender Recognition Based on Fusion of Face and Multi-view Gait
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Language independent voice-based gender identification system
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Practical considerations for real-time implementation of speech-based gender detection
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Pitch-based gender identification with two-stage classification
Security and Communication Networks
| Hi-index | 0.00 |
This paper describes a novel technique specifically developed for gender identification which combines acoustic analysis and pitch. Two sets of hidden Markov models, male and female, are matched to the speech using the Viterbi algorithm and the most likely sequence of models with corresponding likelihood scores are produced. Linear discriminant analysis is used to normalise the models and reduce bias towards a particular gender. An enhanced version of the pitch estimation algorithm used for IMBE speech coding is used to give an average pitch estimate for the speaker. The information provided by the acoustic analysis and pitch estimation are combined using a linear classifier to identify the gender of the speech. The system was tested on three British English databases giving less than 1% identification error rate with two seconds of speech. Further tests without optimisation on eleven languages of the OGI database gave error rates less than 5.2% and an average of 2.0%.