Probablistic modelling of F0 in unvoiced regions in HMM based speech synthesis

HMM based synthesis has attracted great interest due to its compact and flexible modelling of spectral and prosodic parameters. In this approach, short term spectra, fundamental frequency (F0) and duration are simultaneously modelled by multi-stream HMMs. However, since F0 values in unvoiced regions are normally considered as undefined, it is difficult to use standard HMMs for F0 modelling. The currently preferred solution to this is to use a multi-space distribution HMM (MSDHMM) in which discrete distributions are used for modelling the voiced/unvoiced decision and continuous Gaussian distributions are used for modelling the F0 values within the voiced regions. However, the assumption of undefined unvoiced F0 regions and the special structure of the MSDHMM lead to limitations in the accurate modelling of F0 patterns. In this paper an alternative is explored whereby unvoiced F0 values are assumed to exist and are modelled within the standard HMM framework using a globally tied distribution (GTD). Subjective evaluations show that these regular HMMs with GTD can produce significant improvements in the naturalness of the synthesised speech compared to the MSDHMM, and furthermore, the method is insensitive to the exact method used for unvoiced F0 generation.