Evaluating the potential utility of ASR n-best lists for incremental spoken dialogue systems
نویسندگان
چکیده
The potential of using ASR n-best lists for dialogue systems has often been recognised (if less often realised): it is often the case that even when the top-ranked hypothesis is erroneous, a better one can be found at a lower rank. In this paper, we describe metrics for evaluating whether the same potential carries over to incremental dialogue systems, where ASR output is consumed and reacted upon while speech is still ongoing. We show that even small N can provide an advantage for semantic processing, at a cost of a computational overhead.
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