An Integrative and Discriminative Technique for Spoken Utterance Classification

Abstract

Traditional methods of spoken utterance classification (SUC) adopt two independently trained phases. In the first phase, an automatic speech recognition (ASR) module returns the most likely sentence for the observed acoustic signal. In the second phase, a semantic classifier transforms the resulting sentence into the most likely semantic class. Since the two phases are isolated from each other, such traditional SUC systems are suboptimal. In this paper, we present a novel integrative and discriminative learning technique for SUC to alleviate this problem, and thereby, reduce the semantic classification error rate (CER). Our approach revolves around the effective use of the N-best lists generated by the ASR module to reduce semantic classification errors. The N-best list sentences are first rescored using all the available knowledge sources. Then, the sentence that is most likely to helps reduce the CER are extracted from the N-best lists as well as those sentences that are most likely to increase the CER. These sentences are used to discriminatively train the language and semantic-classifier models to minimize the overall semantic CER. Our experiments resulted in a reduction of CER from its initial value of 4.92% to 4.04% in the standard ATIS task.