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In this paper, we present an efficient look-ahead technique which incorporates the language model knowledge at the earliest possible stage during the search process. This so-called language model look-ahead is built into the time synchronous beam search algorithm using a tree-organized pronunciation lexicon for a bigram language model. The language model look-ahead technique exploits the full knowledge of the bigram language model by distributing the language model probabilities over the nodes of the lexical tree for each predecessor word. We present a method for handling the resulting memory requirements. The recognition experiments performed on the 20 000-word North American Business task (Nov.'96) demonstrate that in comparison with the unigram look-ahead a reduction by a factor of 5 in the acoustic search effort can be achieved without loss in recognition accuracy.

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In this paper we describe the optimization of 'conventional' template matching techniques for connected digit recognition (TI/NIST connected digit corpus). In particular we carried out a series of experiments in which we studied various aspects of signal processing, acoustic modeling, mixture densities and linear transforms of the acoustic vector. After all optimization steps, our best string error rate on the TI/NIST connected digit corpus was 1.71% for single densities and 0.74% for mixture densities.