Performance comparison of KLD and PoG metrics for finding the acoustic similarity between phonemes for the development of a polyglot synthesizer

A polyglot synthesizer is a text-to-speech synthesis system, that converts a mixed-language text into speech with single speaker's voice. The straightforward way to develop such a system is to build multiple language-specific synthesizers or to build a single synthesizer after merging common phonemes. In these cases, either language-switching between languages or influence of one language phoneme on the other in synthetic speech is unavoidable. In this paper, to make a compromise between amount of language-switching and language-influence, Kullback-Leibler-based and Product of likelihood-Gaussians(PoG)-based metrics are used to identify acoustically similar phonemes for the languages Tamil and English. Separate HMM-based polyglot synthesizers are built by merging the acoustically similar phones identified by these two metrics. Performance of these synthesizers are evaluated by means of degraded mean-opinion-score(DMOS). Analysis shows that the synthesizer using PoG-based-metric, performs better with a DMOS of 3.54 and 8% of language-switching and 16.6% language-influence.