Current methods for training statistical language models for recognition and understanding require large annotated corpora. The collection, transcription and labeling of such corpora is a major bottleneck for creating new applications and for refinements of existing ones. Thus, it is of great interest to develop methods for automatically learning vocabulary, grammar and semantics from a speech corpus without transcriptions. In this paper we report on an experiment where acoustic morphemes are automatically acquired from the output of a task-independent phone recognizer. The utility of these units is experimentally evaluated for call-type classification in the How may I help you? task. Detected occurrences of the acoustic morphemes in the lattice output provide the basis for the classification of the test sentences. Using lattices, we achieve a reduction of 59% from the false rejection rate using best paths, albeit with a 5% reduction in the correct classification performance from that baseline.
Keywords: Spoken language understanding, Salient phrase acquisition, Acoustic morphemes, Phone lattices.
Cite as: Petrovska-Delacrétaz, D., Gorin, A.L., Wright, J.H., Riccardi, G. (2000) Detecting acoustic morphemes in lattices for spoken language understanding. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 4, 53-56
@inproceedings{petrovskadelacretaz00_icslp,
author={Dijana Petrovska-Delacrétaz and Allen L. Gorin and Jerry H. Wright and Giuseppe Riccardi},
title={{Detecting acoustic morphemes in lattices for spoken language understanding}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 4, 53-56}
}