Hierarchical English emphatic speech synthesis based on HMM with limited training data
Fanbo Meng, Zhiyong Wu, Helen M. L. Meng, Jia Jia, Lianhong Cai · 2012
Emphasis is an important form of expressiveness in speech. Hidden Markov model (HMM) based speech synthesis has shown great flexibility in generating expressive speech. This paper proposes a hierarchical model based on HMMs aiming at synthesizing emphatic speech of both high emphasis quality and high naturalness with the limited amount of data. 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. Experiments show that the proposed hierarchical model can synthesize emphatic speech with high quality for both naturalness and emphasis, using limited amount of training data. Index Terms: emphatic speech synthesis, hidden Markov model (HMM), hierarchy, compensation model 1.