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This paper presents a fast adaptation technique to build a hidden Markov model (HMM) based dialectal speech synthesis acoustic model. Standard Malay is used as a source language whereas Kelantanese Malay is chosen to be target language in this study. Kelantan dialect is a Malay dialect from the northeast of Peninsular Malaysia. One of the most important steps and time consuming in building a HMM acoustic model is the alignment of speech sound. A good alignment will produce a clear and natural synthesize speech. The importance of this study is to propose a quick approach for aligning and building a good dialectal speech synthesis acoustic model by using a different source acoustic model. There are two proposed adaptation approaches in this study to synthesize dialectal Malay sentences using different amount of target speech and a source acoustic model to build the target acoustic model of speech synthesis system. From the results, we found out that the dialectal speech synthesis system built with adaptation approaches are much better in term of speech quality compared to the one without applying adaptation approach.

In this paper, we propose a post decoding system combination approach for automatic transcribing Malay broadcast news. This approach combines the hypotheses produced by parallel automatic speech recognition (ASR) systems. Each ASR system uses different language models, one which is generic domain model and another is domain specific model. The main idea is to take advantage of different ASR knowledge to improve ASR decoding result. It uses the language score and time information to produce a 1-best lattice, and then rescore the 1-best lattice to get the most likely word sequence as the final output. The proposed approach was compared with conventional combination approach, the recognizer output voting error reduction (ROVER). Our proposed approach improved the word error rate (WER) from 33.9% to 30.6% with an average relative WER improvement of 9.74%, and it is better than the conventional ROVER approach.