Biomimetic Pattern Recognition for Speaker-Independent Speech Recognition
Hong Ying Qin, Shoujue Wang, Hua Sun · 2006
In speaker-independent speech recognition, the disadvantage of the most diffused technology (hidden Markov models) is not only the need of many more training samples, but also long train time requirement. This paper describes the use of biomimetic pattern recognition (BPR) in recognizing some Mandarin speech in a speaker-independent manner. The vocabulary of the system consists of 15 Chinese dish's names. Neural networks based on multi-weight neuron (MWN) model are used to train and recognize the speech sounds. Experimental results are presented to show that the system, which can carry out real time recognition of the persons from different provinces speaking common Chinese speech, outperforms HMMs especially in the cases of samples of a finite size