Iterative unsupervised speaker adaptation for batch dictation

This paper describes an automatic batch-style dictation paradigm in which the entire dictated speech is fully utilized for speaker adaptation and is recognized using the speaker adaptation results. The key point is that the same speech data is used both for recognition as the target and for speaker adaptation. Two steps, speech recognition and speaker adaptation which uses recognition results as means of supervision, are iterated to maximize the advantage of closed-data speaker adaptation. Recognition errors are reduced by 37% in a practical application of batch-style speech-to-text conversion of recorded dictation of Japanese medical diagnoses compared to speaker-independent recognition. To select only reliable recognition results, a supervision improvement procedure is used by which erroneous recognition results can be eliminated from the supervision. In this procedure, 59-74% of the data are extracted from the tentative recognition results and their reliability is 89-93% This procedure also reduces recognition errors by 45%.