Investigating Critical Speech Recognition Errors in Spoken Short Messages

Understanding dictated short-messages requires the system to perform speech recognition on the user’s speech. This speech recognition process is prone to errors. If the system can automatically detect the presence of an error, it can use dialog to clarify or correct its transcript. In this work, we present our analysis on what types of errors a recognition system makes, and propose a method to detect these critical errors. In particular, we distinguish between simple and critical errors, where the meaning in the transcript is not the same as the user dictated. We show that our method outperforms standard baseline techniques by 2% absolute F-score.