Mis-recognized utterance detection using hierarchical language model

In this paper, a mis-recognized utterance detection and modification scheme is proposed to recover speech recognition errors in speech translation. In a speech recognition stage, mis-recognition is frequently observed. The most of mis-recognitions result from mis-match of acoustic models and out-of-vocabulary (OOV) words. To cope with both acoustic model mis-match and OOVs, we adopt a hierarchical language model to identify them. A hierarchical language model can generate both hypotheses with and without OOVs (or acoustic mis-matched words). Likelihood difference of these hypotheses is used as utterance confidence measure. To confirm the possibility of this scheme, as a first experiment, we have conducted speech recognition experiments and mis-recognized utterance detection. Experiment results showed 99% detection rate for utterances with OOVs. This rate is considerably higher than 94% of a conventional detection method using a-posteriori probability. The rate of 80%, which is comparable to a conventional method were obtained for the utterances without OOVs. These results support the possibility of the proposed error detection and modification scheme.