MLP-based phone boundary refining for a TTS database

The automatic labeling of a large speech corpus plays an important role in the development of a high-quality Text-To-Speech (TTS) synthesis system. This paper describes a method for the automatic labeling of speech signals, which mainly involves the construction of a large database for a TTS synthesis system. The main objective of the work involves the refinement of an initial estimation of phone boundaries which are provided by an alignment, based on a Hidden Markov Model. A multilayer perceptron (MLP) was employed to refine the phone boundaries. To increase the accuracy of phoneme segmentation, several specialized MLPs were individually trained based on phonetic transition. The optimum partitioning of the entire phonetic transition space and the corresponding MLPs were constructed from the standpoint of minimizing the overall deviation from the hand-labeling position. The experimental results showed that more than 93% of all phone boundaries have a boundary deviation from a reference position smaller than 20 ms. We also confirmed that the database constructed using the proposed method produced results that were perceptually comparable to a hand-labeled database, based on subjective listening tests.