NYU/BBN 1994 CSR Evaluation

NYU’s research objective is to determine whether non-local, linguistically-based word preferences can be used to enhance speech recognition. We are working jointly with BBN, and our system takes as input the N-best hypotheses generated by BBN (with acoustic and n-gram language model scores for each hypothesis). Our goal is to generate scores based on both intersentential dependencies (related to topic coherence) and intrasentential dependencies (connected by syntactic relations) to complement the usual contiguous-word (n-gram) dependencies. We describe our sublanguagemodel, which is intended to capture the effects on vocabulary of topic coherence within an article. We report several measures of this model, including its effect on word error rate when combined with the BBN acoustic and language model scores. We also briefly describe our initial efforts at applying a syntactic language model, and a word model using syntactic relations (a “semantic” model).