Generating effective confirmation and guidance using two-level confidence measures for dialogue systems

We present a method to generate effective confirmation and guidance using concept-level confidence measures (CM) derived from speech recognizer output in order to handle speech recognition errors. We define two conceptlevel CM, which are on content-words and on semanticattributes, using 10-best outputs of the speech recognizer and parsing with phrase-level grammars. Content-word CM is useful for selecting plausible interpretations. Less confident interpretations are given to confirmation process, and non-confident ones are rejected. The strategy improved the interpretation accuracy by 11.5%. Moreover, the semantic-attribute CM is used to estimate user’s intention and generates system-initiative guidances even when successful interpretation is not obtained.