Phonetic transcription of text is an indispensable component of text-to-speech (TTS) systems and is used in acoustic modeling for speech recognition and other natural language processing applications. One approach to the transcription of written text into phonetic entities or sounds is to use a set of well-defined context and language-dependent rules. The process of transcribing text into sounds starts by preprocessing the text and representing it by lexical items to which the rules are applicable. The rules can be segregated into phonemic and phonetic rules. Phonemic rules operate on graphemes to convert them into phonemes. Phonetic rules operate on phonemes and convert them into context-dependent phonetic entities with actual sounds. Converting from written text into actual sounds, developing a comprehensive set of rules, and transforming the rules into implementable algorithms for any language cause several problems that have their origins in the relative lack of correspondence between the spelling of the lexical items and their sound contents. For Standard Malay (SM) these problems are not as severe as those for languages of complex spelling systems, such as English and French, but they do exist. In this paper, developing a comprehensive computerized system for processing SM text and transcribing it into phonetic entities and evaluating the performance of this system, irrespective of the application, is discussed. In particular, the following issues are dealt with in this paper: (1) the spelling and other problems of SM writing and their impact on converting graphemes into phonemes, (2) the development of a comprehensive set of grapheme-to-phoneme rules for SM, (3) a description of the phonetic variations of SM or how the phonemes of SM vary in context and the development of a set of phoneme-to-phonetic transcription rules, (4) the formulation of the phonemic and phonetic rules into algorithms that are applicable to the computer-based processing of input SM text, and (5) the evaluation of the performance of the process of converting SM text into actual sounds by the above mentioned methods.

The development of an HMM-based speech synthesis system for a new language requires resources like speech database and segment-phonetic labels. As an under-resourced language, Malay lacks the necessary resources for the development of such a system, especially segment-phonetic labels. This research aims at developing an HMM-based speech synthesis system for Malay. We are proposing the use of two types of training HMMs, which are the benchmark iterative training incorporating the DAEM algorithm and isolated unit training applying segment-phonetic labels of Malay. The preferred method for preparing segment-phonetic labels is the automatic segmentation. The automatic segmentation of Malay speech database is performed using two approaches which are uniform segmentation that applies fixed phone duration, and a cross-lingual approach that adopts the acoustic model of English. We have measured the segmentation error of the two segmentation approaches to ascertain their relative effectiveness. A listening test was used to evaluate the intelligibility and naturalness of the synthetic speech produced from the iterative and isolated unit training. We also compare the performance of the HMM-based speech synthesis system with existing Malay speech synthesis systems.