Aperiodicity Analysis for Quality Estimation of Text-to-Speech Signals

This contribution presents a new approach towards nonintrusive quality assessment of text-to-speech (TTS) signals. Perturbation measures which capture the degree of excitationspecific aperiodicity in voiced speech are investigated concerning their quality implications in synthesized speech. Based on two independent TTS databases for which formal attributebased listening tests have been conducted, we show that perturbation measures are sensitive to quality aspects of prosody and voice characteristic. Furthermore a dominant dependency on TTS type, namely non-uniform unit-selection and diphone synthesis, is identified. Yet, considerable differences between male and female TTS samples are recognized, emphasizing the need for gender-specific quality assessment.