Automatic Speech Recognition for Thai Sentence based on MFCC and CNNs

Abstract

An automatic speech recognition (ASR) is more important, especially in the Coronavirus outbreak. ASR for Thai sentence was proposed based on MFCC and CNNs in this research. The MFCC features image created from the Thai speech procedure is explained. The MFCC image is treated as a normal image. Object detection techniques based on CNNs can be used to detect Thai words in the frequency image. You Only Look Once (YOLO) is selected as the word localizer and classifier due to its performance and accuracy. The airport service scenario is explored in this research in order to obtain the performance of the proposed system. The airport information system is selected for the experiments. Speeches were collected from 60 participants with 50% males and 50% females. Speech images are constructed based on MFCC and labeled for specific Thai keywords. The YOLOv3 and Tiny YOLOv3 were trained and the performance was evaluated. Clearly, Tiny YOLOv3 network is good enough for this experiment. New speech data provided from new 20 participants were used to test the proposed system. Resulting in the proposed ASR system based on MFCC and CNNs has a good performance in both speed and accuracy.