12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

An Accurate and Robust Gender Identification Algorithm

Andrea DeMarco, Stephen J. Cox

University of East Anglia, UK

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.

Full Paper

Bibliographic reference.  DeMarco, Andrea / Cox, Stephen J. (2011): "An accurate and robust gender identification algorithm", In INTERSPEECH-2011, 2429-2432.