Detecting acoustic morphemes in lattices for spoken language understanding
Dijana Petrovska‐Delacrétaz, Allen L. Gorin, Jerry H. Wright, Giuseppe Riccardi · 2000
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.