In-document adaptation for a human guided automatic transcription service

Abstract

In this work, the task is to assist human transcribers to produce, for example, interview or parliament speech transcriptions. The system will perform in-document adaptation based on a small amount of manually corrected automatic speech recognition results. The corrected segments of the spoken document are used to adapt the speech recognizer’s acoustic and language model. The updated models are used in second-pass recognition to produce a more accurate automatic transcription for the remaining uncorrected parts of the spoken document. In this work we evaluate two common adaptation methods for speech data in settings that represent typical transcription tasks. For adapting the acoustic model we use the Maximum A Posteriori adaptation method. For adapting the language model we use linear interpolation. We compare results of supervised adaptation to unsupervised adaptation, and evaluate the total benefit of using human corrected segments for in-document adaptation for typical transcription tasks.