Task-specific adaptation of speech recognition models

Abstract

Most published adaptation research focuses on speaker adaptation, and on adaptation for noisy channels and background environments. We study acoustic, grammar, and combined acoustic and grammar adaptation for creating task-specific recognition models. Comprehensive experimental results are presented using data from natural language quotes and a trading application. The results show that task adaptation gives substantial improvements in both utterance understanding accuracy, and recognition speed.