. Initially introduced in the late 1960s and early 1970s, dynamic programming algorithms have become increasingly popular in automatic speech recognition. There are two reasons why this has occurred: First, the dynamic programming strategy can be combined with avery e#cient and practical pruning strategy so that very large search spaces can be handled. Second, the dynamic programming strategy has turned out to be extremely #exible in adapting to new requirements. Examples of such requirements are the lexical tree organization of the pronunciation lexicon and the generation of a word graph instead of the single best sentence. In this paper, we attempt to systematically review the use of dynamic programming search strategies for small#vocabulary and large#vocabulary continuous speech recognition. The following methods are described in detail: search using a linear lexicon, search using a lexical tree, language-model look-ahead and word graph generation. 1 Introduction Search strategie...

. This paper presents two look-ahead techniques for large vocabulary continuous speech recognition. These two techniques, which are referred to as language model look-ahead and phoneme look-ahead, are incorporated into the pruning process of the time-synchronous one-pass beam search algorithm. The search algorithm is based on a tree-organized pronunciation lexicon in connection with a bigram language model. Both look-ahead techniques have been tested on the 20 000-word NAB'94 task (ARPA North American Business Corpus). The recognition experiments show that the combination of bigram language model look-ahead and phoneme look-ahead reduces the size of search space by a factor of about 27 without affecting the word recognition accuracy. 1 Introduction In this paper, we describe two look-ahead techniques for improved beam search, namely language model look-ahead and phoneme look-ahead, for large vocabulary continuous speech recognition. The basic idea of the language model look-ahead is t...

In this paper, several techniques for reducing the search complexity of beam search for continuous speech recognition task are proposed. Six heuristic methods for pruning are described and the parameters of the pruning are adjusted to keep constant the word error rate while reducing the computational complexity and memory demand. The evaluation of the effect of each pruning method is performed in Mixture Stochastic Trajectory Model (MSTM). MSTM is a segment-based model using phonemes as the speech units. The set of tests in a speaker-dependent continuous speech recognition task shows that using the pruning methods, a substantial reduction of 67# of search effort is obtained in term of number of hypothesised phonemes during the search. All proposed techniques are independent of the acoustic models and therefore are applicable to other acoustic modeling techniques.