Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition
Heriberto Cuayáhuitl, Nina Dethlefs, Helen Hastie, Oliver Lemon · 2013
A challenge in dialogue act recognition is the mapping from noisy user inputs to dialogue acts. In this paper we describe an approach for re-ranking dialogue act hypotheses based on Bayesian classifiers that incorporate dialogue history and Automatic Speech Recognition (ASR) N-best information. We report results based on the Let’s Go dialogue corpora that show (1) that including ASR N-best information results in improved dialogue act recognition performance (+7 % accuracy), and (2) that competitive results can be obtained from as early as the first system dialogue act, reducing the need to wait for subsequent system dialogue acts. 1