Speech Features Analysis for Tone Language Speaker Discrimination Systems

Abstract

In this paper, a speech pattern analysis framework for tone language speaker discrimination systems is proposed. We hold the hypothesis that speech feature variability is an efficient means for discriminating speakers. To achieve this, we exploit prosody-related acoustic features (pitch, intensity and glottal pulse) of corpus recordings obtained from male and female speakers of varying age categories: children (0–15), youths (16–30), adults (31–50), seniors (above 50)—and captured under suboptimal conditions. The speaker dataset was segmented into three sets: train, validation and test set—in the ratio of 70%, 15% and 15%, respectively. A 41 × 14 self-organizing map (SOM) architecture was then used to model the speech features, thereby determining the relationship between the speech features, segments and patterns. Results of a speech pattern analysis indicated wide F0 variability amongst children speakers compared with other speakers. This gap however closes as the speaker ages. Further, the intensity variability among speakers was similar across all speaker classes/categories, while glottal pulse exhibited significant variation among the different speaker classes. Results of SOM feature visualization confirmed high inter-variability—between speakers, and low intra-variability—within speakers.