Efficient and reliable perceptual weight tuning for unit-selection text-to-speech synthesis based on active interactive genetic algorithms: A proof-of-concept

Abstract

Unit-selection speech synthesis is one of the current corpus-based text-to-speech synthesis techniques. The quality of the generated speech depends on the accuracy of the unit selection process, which in turn relies on the cost function definition. This function should map the user perceptual preferences when selecting synthesis units, which is still an open research issue. This paper proposes a complete methodology for the tuning of the cost function weights by fusing the human judgments with the cost function, through efficient and reliable interactive weight tuning. To that effect, active interactive genetic algorithms (aiGAs) are used to guide the subjective weight adjustments. The application of aiGAs to this process allows mitigating user fatigue and frustration by improving user consistency. However, it is still unfeasible to subjectively adjust the weights of the whole corpus units (diphones and triphones in this work). This makes it mandatory to perform unit clustering before conducting the tuning process. The aiGA-based weight tuning proposal is evaluated in a small speech corpus as a proof-of-concept and results in more natural synthetic speech when compared to previous objective and subjective-based approaches.