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We describe a new algorithm for finding the hypothesis in a recognition lattice that is expected to minimize the word error rate (WER). Our approach thus overcomes the mismatch between the word-based performance metric and the standard MAP scoring paradigm that is sentence-based, and that can lead to sub-optimal recognition results. To this end we first find a complete alignment of all words in the recognition lattice, identifying mutually supporting and competing word hypotheses. Finally, a new sentence hypothesis is formed by concatenating the words with maximal posterior probabilities. Experimentally, this approach leads to a significant WER reduction in a large vocabulary recognition task. 1. INTRODUCTION Word lattices are used by most speech recognizers as a compact representation of a set of alternative hypotheses. In the standard MAP decoding approach 1 the recognizer outputs the string of words corresponding to the path with the highest posterior probability given the acous...
Mangu et al. (1999) studied this question.