Quantized F0 Context and Its Applications to Speech Synthesis, Speech Coding, and Voice Conversion

This paper describes a technique for language-independent prosody modeling using unsupervised prosodic labelling in HMM-based speech synthesis and shows its applications to low bit-rate speech coding and speaker-independent voice conversion. In the proposed technique, sequences of prosodic features are roughly quantized at a phone level and the resultant indexes are used as the prosodic context for the model training. The conventional HMM-based speech synthesis requires accurate prosodic labels corresponding to the speech samples where manual modification is necessary to improve the modeling accuracy, which sometimes takes extra costs and limits its application. In contrast, the proposed technique creates the prosodic label from the training data itself and can apply not only to the speech synthesis but also to the speech coding and voice conversion. Subjective experimental results show the effectiveness of the use of the quantized F0 context without manual prosodic labelling.