DTW-based feature selection for speech recognition and speaker recognition

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.