Sixth International Conference on Spoken Language Processing
We are interested in on-line acquisition of acoustic, lexical and semantic units from spontaneous speech. Traditional ASR techniques require the domain-specific knowledge of acoustic, lexicon data and more importantly the word probability distributions. In this paper we propose an algorithm for unsupervised learning of acoustic and lexical units from out-of-domain speech data. The new lexical units are used for fast adaptation of language model probabilities to a new domain. We show that starting from the Switchboard corpus (lexicon and language model) we learn the most relevant language statistics of the "How May I Help?" task.
Bibliographic reference. Riccardi, Giuseppe (2000): "On-line learning of acoustic and lexical units for domain-independent ASR", In ICSLP-2000, vol.4, 45-48.