A selection method of speech vocabulary for human-robot speech interaction

Speech is the most natural and efficient way for Human-Robot Interaction (HRI), although speech recognition systems face some challenges on a mobile robot platform due to the wide range of users and varied noisy environments. This paper proposes a selection method of speech vocabulary for HRI, which can choose the most robust sub-vocabulary from the predefined isolated word vocabulary. We define a new concept, called Word Robustness, to represent the robustness of a word to speaker-independent and noise. The algorithm for computing Word Robustness is given based on Hidden Markov Model (HMM), then the most robust sub-vocabulary can be selected based on it. For convenience, this method makes use of selecting vocabulary to avoid other procedures such as speaker-adaption. Experiments were achieved based on an isolated word recognition system using a speech database which includes more than ten thousands of speech signals recorded in quiet laboratory and noisy environment respectively. Several sub-sets of vocabulary were selected for robot control based on Word Robustness. The best speaker-independent word recognition rate is 95.19% in noisy environments. Experimental results demonstrate the effectiveness of Word Robustness and the selection method.