Dynamic programming search for continuous speech recognition

The authors gives a unifying view of the dynamic programming approach to the search problem. They review the search problem from the statistical point-of-view and show how the search space results from the acoustic and language models required by the statistical approach. Starting from the baseline one-pass algorithm using a linear organization of the pronunciation lexicon, they have extended the baseline algorithm toward various dimensions. To handle a large vocabulary, they have shown how the search space can be structured in combination with a lexical prefix tree organization of the pronunciation lexicon. In addition, they have shown how this structure of the search space can be combined with a time-synchronous beam search concept and how the search space can be constructed dynamically during the recognition process. In particular, to increase the efficiency of the beam search concept, they have integrated the language model look-ahead into the pruning operation. To produce sentence alternatives rather than only the single best sentence, they have extended the search strategy to generate a word graph. Finally, they have reported experimental results on a 64 k-word task that demonstrate the efficiency of the various search concepts presented.