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In this paper, we study the use of morphological and syntactic context features to improve speech recognition of a morphologically rich language like Arabic. We examine a variety of syntactic features, including part-of-speech tags, shallow parse tags, and exposed head words and their non-terminal labels both before and after the word to be predicted. Neural network LMs are used to model these features...
We report word error rate improvements with syntactic features using a neural probabilistic language model through N-best re-scoring. The syntactic features we use include exposed head words and their non-terminal labels both before and after the predicted word. Neural network LMs generalize better to unseen events by modeling words and other context features in continuous space. They are suitable...
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