Intelligibility analysis of fast synthesized speech
Cassia Valentini-Botinhao, Markus Toman, Michael Pucher, Dietmar Schabus, Junichi Yamagishi · 2014
In this paper we analyse the effect of speech corpus and com-pression method on the intelligibility of synthesized speech at fast rates. We recorded English and German language voice tal-ents at a normal and a fast speaking rate and trained an HSMM-based synthesis system based on the normal and the fast data of each speaker. We compared three compression methods: scal-ing the variance of the state duration model, interpolating the duration models of the fast and the normal voices, and applying a linear compression method to generated speech. Word recog-nition results for the English voices show that generating speech at normal speaking rate and then applying linear compression resulted in the most intelligible speech at all tested rates. A similar result was found when evaluating the intelligibility of the natural speech corpus. For the German voices, interpolation was found to be better at moderate speaking rates but the linear method was again more successful at very high rates, for both blind and sighted participants. These results indicate that using fast speech data does not necessarily create more intelligible voices and that linear compression can more reliably provide higher intelligibility, particularly at higher rates. Index Terms: fast speech, HMM-based speech synthesis, blind users