Part-of-speech effects on text-to-speech synthesis

Abstract

One of the goals of text-to-speech (TTS) systems is to produce natural-sounding synthesized speech. Towards this end various natural language processing (NLP) tasks are performed to model the prosodic aspects of the TTS voice. One of the fundamental NLP tasks being used is the part-of-speech (POS) tagging of the words in the text. This paper investigates the effects of POS information on the naturalness of a hidden Markov model (HMM) based TTS voice when additional resources are not available to aid in the modeling of prosody. It is found that, when a minimal feature set is used for the HMM context labels, the addition of POS tags does improve the naturalness of the voice. However, the same effect can be accomplished by including segmental counting and positional information instead of the POS tags.