An accurate and robust gender identification algorithm
Andrea DeMarco, Stephen Cox · 2011
We describe a robust, unsupervised method of automatic gender identification from speech. We first design a baseline gender classifier based on MFCC features, and add a second classifier that uses context-dependent but text-independent pitch features. The results of these classifiers are then examined for disagree- ments in gender classification. Any disagreements are resolved by the use of a novel pitch-shifting mechanism applied to the ut- terances. We show how the acoustic context classifier provides very good gender identification results, and how these are fur- ther enhanced by the pitch-shifting process. Furthermore this enhancement is preserved across a set of different corpora.