5th European Conference on Speech Communication and Technology

Rhodes, Greece
September 22-25, 1997

Automatic Gender Identification Under Adverse Conditions

Stefan Slomka, Sridha Sridharan

Speech Research Laboratory, Signal Processing Research Center, Queensland University of Technology, Brisbane, Australia

This paper evaluates 63 Automatic Gender Identification (AGI) systems for text-independent clean speech segments, coded speech and speech segments affected by reverberation. The AGI systems contain a Linear Classifier (LC) with inputs from a combination of two average pitch detection methods and paired Gaussian Mixture Models trained with mel-cepstral, autocorrelation, reflection and log area ratios parameterised speech data. An AGI system is built which is able to handle the LPC10, CELP and GSM coders with no significant loss in accuracy and reduce the impact of even severe reverberation by subjecting the training data of the LC with a different room response. Using speech segments with an average duration of 890ms (after silence removal), the best AGI system had an accuracy of 98.5% averaged over all clean and adverse conditions.

Full Paper

Bibliographic reference.  Slomka, Stefan / Sridharan, Sridha (1997): "Automatic gender identification under adverse conditions", In EUROSPEECH-1997, 2307-2310.