Context-dependent labels for an HMM-based speech synthesis system for Malay HMM-based speech synthesis system for Malay

In a Hidden Markov Model-based speech synthesis system, an arbitrary text input has to be converted to context-dependent labels before it can be synthesized. This research proposes the development of a context-dependent label generating module for Malay, an under-resourced language with no available system to generate context-dependent labels. We have developed a grapheme-to-phoneme database and identified the contextual factors of Malay to generate the context-dependent labels for synthesizing Malay speech using an HMM-based speech synthesis system. We assessed the effectiveness of the generating module through intelligibility and naturalness evaluation of the synthetic utterances and these confirm the effectiveness of the proposed generating module. The high scores of intelligibility and naturalness indicate the accuracy of the synthesis labels generated by the generating module for any text input.