11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Augmentation of Adaptation Data

Ravichander Vipperla, Steve Renals, Joe Frankel

University of Edinburgh, UK

Linear regression based speaker adaptation approaches can improve Automatic Speech Recognition (ASR) accuracy significantly for a target speaker. However, when the available adaptation data is limited to a few seconds, the accuracy of the speaker adapted models is often worse compared with speaker independent models. In this paper, we propose an approach to select a set of reference speakers acoustically close to the target speaker whose data can be used to augment the adaptation data. To determine the acoustic similarity of two speakers, we propose a distance metric based on transforming sample points in the acoustic space with the regression matrices of the two speakers. We show the validity of this approach through a speaker identification task. ASR results on SCOTUS and AMI corpora with limited adaptation data of 10 to 15 seconds augmented by data from selected reference speakers show a significant improvement in Word Error Rate over speaker independent and speaker adapted models.

Full Paper

Bibliographic reference.  Vipperla, Ravichander / Renals, Steve / Frankel, Joe (2010): "Augmentation of adaptation data", In INTERSPEECH-2010, 530-533.