Subword lexical modelling for speech recognition
Raymond Y.K. Lau, Stephanie Seneff · DSpace@MIT (Massachusetts Institute of Technology) · 1998
In this work, we introduce and develop a novel framework, ANGIE, for modelling subword lexical phenomena in speech recognition. Our framework provides a flexible and powerful mechanism for capturing morphology, syllabification, phonology, and other subword effects in a hierarchical manner which maximizes sharing of subword structures. ANGIE models the subword structure within a context-free grammar and an accompanying probability model. We believe that our framework has several advantages: The sharing mechanism allows training data to be pooled amongst instances of the same word substructure even when they occur across different words in the lexicon. Further, knowledge of this substructure can be extended to filler models in a word-spotter, new words added incrementally to a recognizer's vocabulary, and potentially in support of new word detection. The context-free foundation allows for ease of research and experimentation with varying subword representations, and also facilitates integration with a natural language understanding system. Finally, the availability of subword structural information in a recognition system enables exploration of prosodic models which use this information.