Abstract: This paper presents a novel feature (Weighted Global Time-Frequency feature, i.e WGTF) for confusing word speech recognition, which enhances the difference among different confusing words by selecting proper base fuctions and weighting functions. Meanwhile, the storng discriminative power of artificial neural network has been used as a classifier to further raise the recognition rate. The experiment shows that the proposed method outperforms the standard DHMM and other ANN-based method.

Key words: vocal tract model, GTF feature, neural network, speech recognition, weighting function