A novel approach of system design for dialect speech interaction with NAO robot

Intelligent human robot interaction are becoming popular in both industry and academia. However, amongst current techniques, speech recognition is a challenging topic, including real-time translation with high accuracy, amicability and the support for recognizing minor languages or sophisticated dialects. In this paper, we propose a human-friendly prototype deployed on NAO robots in a real-life scenario through daily speech commands and NAO would act accordingly. We primarily adopt HMM-GMM, the combination of HMMs (Hidden Markov Models) and GMMs (Gaussian Mixtures Models). The experimental results show that the proposed prototype achieves high accuracy and well-received by experiment subjects.