This thesis describes the research on the use of non-linear formant trajectories to model speech dynamics under the framework of a multiple-level segmental hidden Markov model (MSHMM). The particular type of intermediate-layer model investigated in this study is based on the 12-dimensional parallel formant synthesiser (PFS) control parameters, which can be directly used to synthesise speech with a formant synthesiser. The non-linear formant trajectories are generated by using the speech parameter generation algorithm proposed by Tokuda and colleagues. The performance of the newly developed non-linear trajectory model of dynamics is tested against the piecewise linear trajectory model in both speech recognition and speech synthesis. In speech synthesis experiments, the 12 PFS control parameters and their time derivatives are used as the feature vectors in the HMM-based text-to-speech system. The human listening test and objective test results show that, despite the low overall quality of the synthetic speech, the non-linear trajectory model of dynamics can significantly improve the intelligibility and naturalness of the synthetic speech. Moreover, the generated non-linear formant trajectories match actual formant trajectories in real human speech fairly well. The \(\char{cmmi10}{0x4e}\)-best list rescoring paradigm is employed for the speech recognition experiments. Both context-independent and context-dependent MSHMMs, based on different formant-to-acoustic mapping schemes, are used to rescore an \(\char{cmmi10}{0x4e}\)-best list. The rescoring results show that the introduction of the non-linear trajectory model of formant dynamics results in statistically significant improvement under certain mapping schemes. In addition, the smoothing in the non-linear formant trajectories has been shown to be able to account for contextual effects such as coarticulation.
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Towards an improved model of dynamics for speech recognition and synthesis
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