An Accurate and Robust Gender Identification Algorithm

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 disagreements in gender classification. Any disagreements are resolved by the use of a novel pitch-shifting mechanism applied to the utterances. We show how the acoustic context classifier provides very good gender identification results, and how these are further enhanced by the pitch-shifting process. Furthermore this enhancement is preserved across a set of different corpora.