Acoustic Features Modelling for Statistical Parametric Speech Synthesis: A Review

The objective of this paper is to present a detailed review of modelling various acoustic features employed in statistical parametric speech synthesis (SPSS). As reported in the literature, many acoustic features have been modelled in SPSS to enhance the synthesis quality. This work studies those approaches that add to the quality of SPSS by including such acoustic features. In particular, several categories of acoustic features that improve the perceptual quality of synthetic speech are discussed. The acoustic features modelling reported in the literature can be broadly classified as F0, vocal-tract, and source features, which primarily represent the prosody, intelligibility, and naturalness of speech, respectively. Besides, SPSS techniques to synthesize speech from these acoustic features and recent advancement in synthesis based on direct waveform generation are also studied in the paper. Finally, the paper concludes with a brief discussion and a mention on the scope in SPSS.