A data-driven organization of the dynamic programming beam search for continuous speech recognition

Abstract

This paper describes a data-driven organization of the dynamic programming beam search for large vocabulary, continuous speech recognition. This organization can be viewed as an extension of the one-pass dynamic programming algorithm for connected word recognition. In continuous speech recognition we are faced with a huge search space, and search hypotheses have to be formed at the 10-ms level. The organization of the search presented has the following characteristics. Its computational cost is proportional only to the number of hypotheses actually generated and is independent of the overall size of the potential search space. There is no limit on the number of word hypotheses, there is only a limit to the overall number of hypotheses due to memory constraints. The implementation of the search has been studied and tested on a continuous speech data base comprising 20672 words.