Rich-Context Unit Selection (RUS) Approach to High Quality TTS

This paper presents a Rich-context Unit Selection (RUS) approach to high quality speech synthesis. Based upon our previous work on rich context modeling, we use the corresponding parametric HMMs to represent waveform units and form a “sausage-like” lattice. A prune-and-search procedure is proposed, in which Kullback-Leibler divergence is adopted to select potential candidate units, and normalized cross-correlation is used as the final objective measure to search for the optimal unit path. The maximum cross-correlation criterion provides the optimal concatenation between successive units, in terms of spectral similarity, phase continuity and best connecting timing instants. Subjectively, both preference and MOS tests were conducted to compare RUS with our current Weight-table based Unit Selection (WUS) synthesis. Experimental results show that the voice quality of synthesized speech is significantly improved by RUS over the conventional WUS.