Detecting acoustic morphemes in lattices for spoken language understanding

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.