The ASR technique for meal service robot

This paper is to apply Automatic Speech Recognition (ASR) technique to the meal service robot in order to the user can be easier to order the meal and increase interaction between the robot and human. So far, there is not any speech recognition module for the robot in the market. In general, modify slightly by using the voice technology and modules directly to be used for the robot. Therefore, this paper is based on Mel-frequency Cepstral coefficients (MFCC) to scratch the characteristic parameters and make use of Hidden Markov Model (HMM) as the recognition technology and create speech model by spce3200 for meal service robot. The main purpose of this paper is to achieve the design of embedded HMM of speech recognition and reducing the small size, low power, short time consumption. In addition, it can fast finish training speech data and real-time recognition. This paper presents to train 25 sets of Chinese speech data for the meal service robot. The voice output is using record. We test a sentence and multi sentences under the three different environments. The highest recognition rate of a sentence has a successful rate 95% and multi-sentences can have 85% with record. In summary, the integrated test results have a relevant reliability.