Gender identification using a general audio classifier

In the context of content-based multimedia indexing gender identification using speech signal is an important task. Existing techniques are dependent on the quality of the speech signal making them unsuitable for the video indexing problems. In this paper we introduce a novel gender identification approach based on a general audio classifier. The audio classifier models the audio signal by the first order spectrum’s statistics in 1s windows and uses a set of neural networks as classifiers. The presented technique shows robustness to adverse audio compression and it is language independent. We show how practical considerations about the speech in audio-visual data, such as the continuity of speech, can further improve the classification results which attain 92%.