Efficient, High-Performance Algorithms for N-Best Search

We present two efficient search algorithms for real-time spoken language systems. The first called the Word-Dependent N-Best algorithm is an improved algorithm for finding the top N sentence hypotheses. The new algorithm is shown to perform as well as the Exact Sentence-Dependent algorithm presented previously but with an order of magnitude less computation. The second algorithm is a fast match scheme for continuous speech recognition called the Forward-Backward Search. This algorithm, which is directly motivated by the Baum-Welch Forward-Backward training algorithm, has been shown to reduce the computation of a time-synchronous beam search by a factor of 40 with no additional search errors.