Generating emphatic speech with hidden Markov model for expressive speech synthesis

Emphasis plays an important role in expressive speech synthesis in highlighting the focus of an utterance to draw the attention of the listener. As there are only a few emphasized words in a sentence, the problem of the data limitation is one of the most important problems for emphatic speech synthesis. In this paper, we analyze contrastive (neutral versus emphatic) speech recordings considering kinds of contexts, i.e. the relative locations between the syllables and the emphasized words. Based on the analysis, we propose a hidden Markov model (HMM) based method for emphatic speech synthesis with limited amount of data. In this method, decision trees (DTs) are constructed with non-emphasis-related questions using both neutral and emphasis corpora. The data in each leaf node of the DTs are classified into 6 emphasis categories according to the emphasis-related questions. The data in the same emphasis category are grouped into one sub-node and are used to train one HMM. As there might be no data of some specific emphasis categories in the leaf nodes of the DTs, a method based on cost calculation is proposed to select a suitable HMM in the same leaf node for predicting parameters. Further a compensation model is proposed to adjust the predicted parameters. We conduct a series of experiments to evaluate the performances of the approach. Experiments indicate that the proposed emphatic speech synthesis models improve the emphasis quality of synthesized speech while keeping a high degree of the naturalness.