This paper examines an approach for combining two different methods for detecting errors in the output of a speech recognizer. The first method attempts to alleviate recognition errors by using an explicit model for detecting the presence of out-of-vocabulary (OOV) words. The second method identifies potentially misrecognized words from a set of confidence features extracted from the recognitio...

This paper addresses the problem of detecting keywords in unconstrained speech without explicit modeling of nonkeyword segments. The proposed algorithm is based on recent developments in confidence measures using local posterior probabilities, and searches for the segment maximizing the average observation posterior’ along the most likely path in the hypothesized keyword model.’ As known, this ...