Evaluation of Small-Scale Deep Learning Architectures in Thai Speech Recognition

This paper presents a performance evaluation study for small-scale deep learning neural network for Thai speech recognition task. Convolutional neural network and long short term memory networks were built with a relatively small size dataset and small constructs. The aim of this study is to determine which method would be suitable for a small-scale deep learning study. Relatively small speech corpus was used to build deep-learning neural networks with two different architectures, including convolutional neural network (CNN) model and long short term memory (LSTM) model. Models were evaluated using cross validation technique and compare to one another. The result shows that CNN outperformed LSTM for a small-scale deep learning. This suggests that with the limited dataset and small-scale architecture CNN is a more suitable choice in the speech recognition study.