Automatic segmentation and labelling of multi-lingual speech data

A new system for the automatic segmentation and labelling of speech is presented. The system is capable of labelling speech originating from different languages without requiring extensive linguistic knowledge or large (manually segmented and labeled) training databases of that language. The system comprises small neural networks for the segmentation and the broad phonetic classification of the speech. These networks were originally trained on one task (Flemish continuous speech), and are automatically adapted to a new task. Due to the limited size of the neural networks, the segmentation and labelling strategy requires but a limited amount of computations, and the adaptation to a new task can be accomplished very quickly. The system was first evaluated on five isolated word corpora designed for the development of Dutch, French, American English, Spanish and Korean text-to-speech systems. The results show that the accuracy of the obtained automatic segmentation and labelling is comparable to that of human experts. In order to provide segmentation and labelling results which can be compared to data reported in the literature, additional tests were run on TIMIT and on the English, Danish and Italian portions of the EUROMO continuous speech utterances. The performance of our system appears to compare favourably to that of other systems.