Automatic Transformation of Speech Databases for Continuous Speech Recognition

In this paper a method is described to generate automatically the labels for a new speech database from an existing manually labeled speech database. This becomes necessary when new standards are introduced and the speech signals have to be resampled. A dynamic time warping algorithm is used to match the original and the resampled speech signals. The comparison is carried out on mel based features. To improve computation time the search space for the DTW algorithm is restricted. Several experiments were carried out with a normal density Bayes classifier to check the quality of the new labelings. The results showed only a slight decrease in performance when using the new labelings.