Ausdang Thangthai

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This paper presents applications of five famous learning methods for Thai phrase break prediction. Phrase break prediction is particularly important for our Thai text-to-speech synthesizer (TTS), where input Thai text has no word and sentence boundary. The learning methods include a POS sequence model, CART, RIPPER, SLIPPER and neural network. Features(More)
This paper presents naturalness improvement in Thai unit-selection text-to-speech synthesis (TTS) based on prosody modeling. Although several modeling approaches of prosodic parameters in Thai speech have been proposed, they have not been proven to provide a promising performance when practically assembling in a synthesizer. In this paper, two learning(More)
This paper presents a bi-lingual Thai-English text-to-speech synthesis (TTS) system on Android mobile devices. The system deploys a Thai text processor and a well-known open-source English text processor, which can analyzes English text at high intelligibility. With hidden Markov model (HMM) based speech unit and audio streaming optimization, it can(More)
Several Thai TTS systems are already available on a resourceful platform such as a personal computer. However, porting these systems to a resource limited device such as a mobile phone is not an easy task. Practical aspects including application size and processing time have to be concerned. In this paper, we aim at developing a Thai speech synthesizer that(More)