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)
Part-of-speech (POS) has been widely used as the main feature for predicting phrase breaks in text-to-speech synthesis (TTS) systems. However, POS does not clearly represent syntactic information that is necessary for analyzing the grammatical tree structure of a language to assign phrase breaks. Instead of using POS, this paper proposes to use categorial(More)