Improve the Quality of Synthetic Speech Trained with Found Data using Silence Cutter

Abstract

Using found data as training data in statistical parametric speech synthesis can alleviate various problems in tedious database construction. However, the extra silences resided in found data degrades the quality of synthetic speech. Therefore, in this study, silence cutter was created to eliminate the extra silences in the training data. The motivation is the extra silences would be incorrectly assigned to training script and result in unnatural synthetic speech. Therefore, in this study, a Malay speech synthesis system has been constructed using found data from internet. Silence cutter has been utilized to cut out extra silences. The synthetic speech using found data with and without silence cutter was verified and compared to find out the effect of silence cutter. Result showed that silence cutter has help to improve synthetic speech naturalness and reduce the Word Error Rate (WER) in intelligibility test. In short, using found data can alleviate the problem of preparing high quality training data and silence cutter can be used to refine the found data to generate better quality of synthetic speech.