Automatic diagnosis of recognition errors in large vocabulary continuous speech recognition systems

Automatic diagnosis of recognition errors in large vocabulary continuous speech recognition (LVCSR) systems is addressed. It consists of two steps. The first step is to identify the module that causes recognition errors for every erroneous segment. This statistics points out which modules to be revised. The second step is to analyze the causes of the errors in detail. Specifically, the triphone and N-gram entries related to the errors are listed. The diagnostic information provides directions for improvement. This diagnosis has been applied to three LVCSR systems: read speech dictation system, lecture speech transcription system and dialogue speech recognition system. We have observed different and interesting diagnosis results. In the dictation system, the diagnosis is useful for improving our decoder Julius. In the lecture and dialogue speech recognition systems, problems in acoustic and language modeling are made clear.