Phonetic training and language modeling for word spotting

The authors present a view of HMM (hidden Markov model)-based word spotting systems as described by three main components: the HMM acoustic model; the overall HMM structure, including nonkeyword modeling; and the keyword scoring method. They investigate and present comparative results for various approaches to each of these components and show that design choices for these components can be addressed separately. They also present a novel approach to word spotting that combines phonetic training, large vocabulary modeling, and statistical language modeling with a posterior probability approach to keyword scoring. They perform word spotting experiments using telephone quality conversational speech from the Switchboard corpus to examine the effect of different design choices for the three components and demonstrate that the proposed approach provides superior performance to previously used techniques.<>