DTW-Based Feature Selection for Speech Recognition and Speaker Recognition
Jing Liu · 2005
In this paper, a DTW--based graph theoretic method for feature subset selection of speech recognition and speaker recognition is discussed, and a DTW--based directed acyclic graph optimization method (DTWDAG) is proposed. We extend the Euclidean--distance based similarity matrix clustering method to DTW--based similarity matrix clustering, and construct a cost function according to similarity matrix. Combining the cost function with (L--r) optimization algorithm, the method is applied to the isolated digital speaker--dependent speech recognition and text-- dependent speaker identification. The experiment results demonstrate the efficient performance of DTWDAG in feature subset selection processing.