Improving Automatic Call Classification using Machine Translation

Utterance classification is an important task in spoken-dialog systems. The response of the system is dependent on category assigned to the speaker's utterance by the classifier. However, often the input speech is spontaneous and noisy which results in high word error rates. This results in unsatisfactory system performance. In this paper we describe a method to improve the natural language call classification task using statistical machine translation (SMT). We utilize the translation model in SMT to capture the relation between truth and the ASR transcribed text. The model is trained using the human transcribed text and the ASR transcribed text. During deployment SMT is used to sanitize the ASR transcribed text. Our experiments with IBM model 2 shows significant improvement in call classification accuracy.