Classification of gender based on cepstral coefficients and spectral moments.

Abstract

Several gender classification methods based on acoustic information were compared. The data came from 31 native speakers of Romanian (10 males, 21 females). A subset of fricatives and vowels (7348 tokens) was divided by hidden Markov model training into three acoustically uniform regions. For each region, (a) a set of cepstral coefficients, specifically c0–c4 and (b) two sets of spectral moments, specifically bark-transformed and linear moments 1–4 plus rms, were extracted. The acoustic data were then used in a linear discriminant analysis to classify the tokens by gender. The findings show that cepstral coefficients perform better than spectral moments in gender classification, and that spectral moments are more successful than linear moments. The overall correct classification was 90% for the cepstral analysis, 78% for bark spectral moments, and 72% for linear moments. When the data from fricatives and vowels were examined separately, it was found that in all cases the vowel information yielded more accurate gender classification (e.g., 84% correct classification for cepstral moments based on vowels only), but the fricative information alone could account for 78% correct classification. These analyses provide insight into the differential distribution of acoustic features related to gender over segment types.