Exploring the Effect of Tones for Myanmar Language Speech Recognition Using Convolutional Neural Network (CNN)

Abstract

Tone information is very helpful to improve automatic speech recognition (ASR) performance in tonal languages such as Mandarin, Thai, Vietnamese, etc. Since Myanmar language is being considered as a tonal language, the effect of tones on both syllable and word-based ASR performance has been explored. In this work, experiments are done based on the modeling of tones by integrating them into the phoneme set and incorporating them into the Convolutional Neural Network (CNN), state-of-the-art acoustic model. Moreover, to be more effective tone modeling, tonal questions are used to build the phonetic decision tree. With tone information, experiments show that compared with Deep Neural Network (DNN) baseline, the performance of CNN model achieves nearly 2% for word-based ASR or more than 2% for syllable-based ASR improvement over DNN model. As a result, the CNN model with tone information gets 2.43% word error rate (WER) or 2.26% syllable error rate (SER) reductions than without using it.