Evaluating spoken dialogue processing for time-offset interaction

Abstract

This paper presents the first evaluation of a full automated prototype system for time-offset interaction, that is, conversation between a live person and recordings of someone who is not temporally copresent. Speech recognition reaches word error rates as low as 5% with generalpurpose language models and 19% with domain-specific models, and language understanding can identify appropriate direct responses to 60-66% of user utterances while keeping errors to 10-16% (the remainder being indirect, or off-topic responses). This is sufficient to enable a natural flow and relatively open-ended conversations, with a collection of under 2000 recorded statements.