Estimating Word-Stability During Incremental Speech Recognition

Many speech user interfaces can be improved by incrementally displaying or interpreting a speech recognizer's current best path as a user speaks. This gives rise to a problem of instability, whereby the best path may change frequently, particularly with respect to the words most recently spoken. Introducing a lag between the audio most recently processed and the portion of the best path shown to the user can lead to more usable incremental results. In the ideal case, the lag introduced would vary to recover exactly the longest stable prefix of the best path. In this paper, we introduce a framework for estimating a stability statistic for each word, and explore the tradeoff of stability and lag by thresholding stability statistics estimated using a variety of features.