Simultaneous estimation of confidence and error cause in speech recognition using discriminative model

Since recognition errors are unavoidable in speech recognition, confidence scoring, which accurately estimates the reliability of recognition results, is a critical function for speech recognition engines. In addition to achieving accurate confidence estimation, if we are to develop speech recognition systems that will be widely used by the public, speech recognition engines must be able to report the causes of errors properly, namely they must offer a reason for any failure to recognize input utterances. This paper proposes a method that simultaneously estimates both confidences and causes of errors in speech recognition results by using discriminative models. We evaluated the proposed method in an initial speech recognition experiment, and confirmed its promising performance with respect to confidence and error cause estimation.