A reliable multidomain model for speech act classification

In a multidomain dialogue, identifying speech acts is not easy because of the problem of interference between input features. To overcome this problem, we propose a two-step model for speech act classification. In the first step, the proposed model detects a dialogue domain associated with an input utterance. In the second step, the proposed model determines the speech act of the input utterance by using only statistical information about input features in the detected dialogue domain. In the experiment, the precision of the proposed model was higher than that of the baseline system without domain selection by 5.5%. On the basis of this experimental result, we conclude that reducing the interferences between input features by using a domain detection process is effective in improving the precision of speech act classification in multiple domains.