Discriminative approach to lexical entry selection for automatic speech recognition of agglutinative language

In agglutinative languages, selection of lexical unit is not obvious. Morpheme unit is usually adopted to ensure the sufficient coverage, but many morphemes are short, resulting in weak constraints and possible confusions. In this paper, we propose a discriminative approach to select lexical entries which will directly contribute to ASR error reduction. We define an evaluation function for each word by a set of features and their weights, and the measure for optimization by the difference of WERs by the morpheme-based model and by the word-based model. Then, the weights of the features are learned by a perceptron algorithm. Finally, word (or sub-word) entries with higher evaluation scores are selected to be added to the lexicon. This method is successfully applied to an Uyghur large-vocabulary continuous speech recognition system, resulting in a significant reduction of WER and the lexicon size. Further improvement is achieved by combining with a statistical method based on mutual information criterion.