The Development of Northern Thai Dialect Speech Recognition System

This study investigated the necessary ingredients for the development of an automatic speech recognition (ASR) system for the Northern Thai language. Building an ASR model for such an arguably low-resource language poses challenges both in terms of the quantity and the quality of the corpus. The experimental results demonstrated that the current state-of-the-art deep neural network trained in an end-to-end manner, and pre-trained from a closely related language, such as Standard Thai, often outperformed its traditional HMM-based counterparts. The results also suggested that incorporating northern Thai-specific tonal information and augmenting the character-based end-to-end model with an n-gram language model further improves the recognition performance. Surprisingly, the quality of the transcription of the speech corpus was not found to positively correlate with the recognition performance in the case of the end-to-end system. The results show that the end-to-end ASR system was able to achieve the best word error rate (WER) of 0.94 on out-of-sample data. This is equivalent to 77.02% and 60.34% relative word error rate reduction over the 4.09 and 2.37 WERs of the traditional TDNN-HMM and the vanilla deep neural network baselines.