Thai ASR development for network-based speech translation

A network-based multilingual speech translation service under the Universal Speech Translation Advanced Research (U-STAR) consortium requires a well-tuned Thai automatic speech recognition (ASR) service. This paper summarizes the development of the service by utilizing both Thai read-speech and telephone speech (LOTUS-CELL 2.0) corpora. Tuning is performed regarding different sets of acoustic unit and training data. An evaluation shows that the recognition accuracy of ASR working over data channels can be improved by using the LOTUS-CELL 2.0 corpus although the corpus was constructed via voice channels. The problem of Named-entity (NE) words often found in the working domain is obvious and leads to an urgent future work.