Adaptive Speech Synthesis of Albanian Dialects

Abstract

In this paper, we show how adaptive modeling within the statistical parametric speech synthesis framework can be applied to Albanian dialects. We develop speaker dependent voices for the Tosk and Gheg dialect and adapt models for the Gheg dialect from the Tosk models. We show that the adapted Gheg models outperform the speaker dependent Gheg model on an intelligibility and dialect classification task. Furthermore we show that the speaker dependent Tosk model outperforms a formant based synthesizer on an intelligibility, dialect classification and pair-wise comparison task. This formant based synthesizer is the only publicly available synthesizer for Albanian at the moment. We also show that our Gheg and Tosk synthesizers are as intelligible as natural speech. The method where one dialect is modeled through adaptation of a closely related other dialect can be applied to language varieties in general, where the background variety and adapted variety can be chosen based on pragmatic considerations like speaker or data resource availability.