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A Novel Excitation Approach for HMM-Based Speech Synthesis

One of the drawbacks to the speech synthesis technique wherein speech parameters are directly generated from hidden Markov models (HMM-based speech synthesis) is the unnaturalness of the synthesized speech. This problem occurs owing to the rough excitation model employed during the waveform generation stage. This report introduces a new excitation approach that attempt to solve this problem. The proposed scheme consists in feeding the mel log spectrum approximation (MLSA) filter with mixed excitation, obtained through a set of state-dependent filters. The filters are derived from the speech database through a closed-loop procedure where the likelihood of the residual is maximized.