A New Method for Dynamic Time Alignment of Speech Waveforms

In this paper, a new method for dynamic time alignment of speech waveforms is introduced. The method attempts to address the shortcomings of traditional time alignment approaches, commonly based on dynamic programming algorithms. Such methods, usually called dynamic time warping (DTW) algorithms, make the assumption that the samples of the speech waveform under consideration are statistically independent. The proposed method makes no such assumption. Instead, the method is based on models of speech entities with Gaussian distributions and general covariance matrices. These ideas are implemented by employing the branch and bound search algorithm [1] coupled with the Mahalanobis distance measure as the matching criterion. Hence, the new method attempts to utilise more discriminatory information than is presently incorporated. Preliminary results on a spoken letter recognition problem are reported validating the approach.