Autoregressive clustering for HMM speech synthesis

Abstract

The autoregressive HMM has been shown to provide efficient parameter estimation and high-quality synthesis, but in previous experiments decision trees derived from a non-autoregressive system were used.

In this paper we investigate the use of autoregressive clustering for autoregressive HMM-based speech synthesis. We describe decision tree clustering for the autoregressive HMM and highlight differences to the standard clustering procedure. Subjective listening evaluation results suggest that autoregressive clustering improves the naturalness of the resulting speech.

We find that the standard minimum description length (MDL) criterion for selecting model complexity is inappropriate for the autoregressive HMM. Investigating the effect of model complexity on naturalness, we find that a large degree of overfitting is tolerated without a substantial decrease in naturalness.