Generating Training Data for Medical Dictations

In automatic speech recognition (ASR) enabled applications for medical dictations, corpora of literal transcriptions of speech are critical for training both speaker independent and speaker adapted acoustic models. Obtaining these transcriptions is both costly and time consuming. Non-literal transcriptions, on the other hand, are easy to obtain because they are generated in the normal course of a medical transcription operation. This paper presents a method of automatically generating texts that can take the place of literal transcriptions for training acoustic and language models. ATRS is an automatic transcription reconstruction system that can produce near-literal transcriptions with almost no human labor. We will show that (i) adapted acoustic models trained on ATRS data perform as well as or better than adapted acoustic models trained on literal transcriptions (as measured by recognition accuracy) and (ii) language models trained on ATRS data have lower perplexity than language models trained on non-literal data.