Investigating Signal Correlation as Continuity Metric in a Syllable Based Unit Selection Synthesis System

In recent years, text-to-speech (TTS) systems have shown considerable improvement as far as the quality of the synthetic speech is concerned. Data driven synthesis methods using syllable as basic unit for concatenation, have proved to generate high quality speech for Indian Languages because of their advantage of prosodic matching function. However, still there is no acceptable solution to the optimal selection of speech segments in terms of audible discontinuities and human perception. This problem gets aggravated in the cases where there is no enough data for building the voice due to the missing units. In this paper, we continue our efforts in trying to address this by investigating the use of a new continuity measure based on maximum signal correlation for optimal selection of units in concatenative text-to-speech (TTS) synthesis framework. We explore two formulations for calculating the signal correlation: cross correlation (CC) based and average magnitude difference function (AMDF) based. We first perform an initial experiment to understand the significance of the approach and then build 5 experimental systems. Evaluations on 30 sentences for each of these languages by native users of the language show that the proposed continuity measure results in more natural sounding synthesis.