By using human speech information, different kinds of speaker and speech recognition systems have been developed for partner robots to efficiently cooperate with people in the daily life. For improving the recognition accuracy and robustness, a two-stage pattern matching algorithm for speaker recognition system of partner robots is proposed. In the first matching stage, by using fuzzy c-means and declustering in vector quantization(VQ) method, the recognition performance with limited training data is improved. For avoiding the phenomenon of similar cepstral features by different speakers, with three additional speech features, the second stage is designed to rematch the similar recognition results of the first stage. In order to evaluate the proposed structure, some experiments have implemented on a public database ELSDSR and an speech owners database for partner robots. The results verified the proposed method obtained more accurate recognition results with strong robustness.
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