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Problem statement: The main problem with current Malay Text-To-Speech (MTTS) synthesis system is the poor quality of the generated speech sound due to the inability of traditional TTS system to provide multiple choices of unit for generating more accurate synthesized speech. Approach: This study proposes a phonetic context variable length unit selection MTTS system that is capable of providing more natural and accurate unit selection for synthesized speech. It implemented a phonetic context algorithm for unit selection for MTTS. The unit selection method (without phonetic context) may encounter the problem of selecting the speech unit from different sources and affect the quality of concatenation. This study proposes the design of speech corpus and unit selection method according to phonetic context so that it can select a string of continuous phoneme from same source instead of individual phoneme from different sources. This can further reduce the concatenation point and increase the quality of concatenation. The speech corpus was transcribed according to phonetic context to preserve the phonetic information. This method utilizes word base concatenation method. Firstly it will search through the speech corpus for the target word, if the target is found; it will be used for concatenation. If the word does not exist, then it will construct the words from phoneme sequence. Results: This system had been tested with 40 participants in Mean Opinion Score (MOS) listening test with the average rates for naturalness, pronunciation and intelligibility are 3.9, 4.1 and 3.9. Conclusion/Recommendation: Through this study, a very first version of Corpus-based MTTS has been designed; it has improved the naturalness, pronunciation and intelligibility of synthetic speech. But it still has some lacking that need to be perfected such as the prosody module to support the phrasing analysis and intonation of input text to match with the waveform modifier

Implementation of Phonetic Context Variable Length Unit Selection Module for Malay Text to Speech