This paper is to apply Automatic Speech Recognition (ASR) approach to the meal service robot so that the user can be easier to order the meal and increase the convenience of interaction between the robot and human. The Mel-frequency Cepstral coefficients (MFCC) is used to extract the feature from speech signals, and Hidden Markov Model (HMM) is applied as the recognition speech model by spce3200. The proposed approach is based on the embedded HMM for speech recognition and thus reducing the size and power in a less computational time. There are 25 sets of Chinese speech data are trained and tested using a single sentence and multi sentences under different environments. The highest recognition rate in single sentence has confirmed up to 95%, and multi-sentences can reach 85%. Accordingly, the practical results have indicated its relevant reliability. (27 refs)
關聯:
International Journal of Advancements in Computing Technology, IJACT
