Dempster-Shafer Fusion Based Gender Recognition for Speech Analysis Applications

Abstract

Speech signals carry valuable information about the speaker including age, gender, and emotional state. Gender information can act as a vital preprocessing ingredient for enhancing speech analysis applications like adaptive human-machine interfaces, multi-modal security applications, and sophisticated intent and context analysis based forensic systems. In uncontrolled environments like telephone speech applications, the gender recognition system should be adaptive, accurate, and robust to noisy environments. This paper presents a reasoning method governed by Dempster-Shafer theory of evidence for automatic gender recognition from telephone speech. The proposed method uses mel-frequency cepstral coefficients with a support vector machine to generate the initial prediction results for individual speech segments. The reasoning scheme collects and validates results from support vector machine and treats convincing predictions as valid evidence. It is argued that the consideration of valid evidence in the reasoning process improves recognition performance by avoiding unconvincing classification results. Experiments conducted on large speech datasets reveal the superiority of the proposed gender recognition scheme for speech analysis applications.