Discriminative confidence and error cause estimation for extended speech recognition function

Errors are unavoidable in speech recognition, and so confidence estimation, which scores the reliability of recognition results, plays a critical role in this procedure. If we are to develop speech recognition systems capable of practical use, in addition to achieving accurate confidence estimation, we will need to extend the functions of speech recognition engines. As the first step towards extending these functions, we have proposed a method that estimates the causes of recognition errors while simultaneously estimating the confidence of recognition results using a discriminative model, and shown its potential experimentally. In this paper, we modify our previously proposed method by dividing its simultaneous confidence and error cause estimation procedure into two separate procedures. In the speech recognition experiments, the separate estimation methods achieved the same confidence estimation accuracy as the simultaneous method but their error cause estimation accuracies were superior.