Studies in transformation-based adaptation

This paper studies the use of transformation-based speaker adaptation in improving the performance of large vocabulary continuous speech recognition systems. We present a formulation of the adaptation procedure that is simpler than existing methods. Our experiments demonstrate that speaker normalization continues to be important even after significant amounts of speaker adaptation. An automatic clustering algorithm is compared to human expertise in sorting output distributions into collections that share the same transformation. We quantify improvements over standard Bayesian (by maximum a posteriori or MAP) adaptation in terms of (a) speed of adaptation, and (b) robustness to transcription errors. Finally, we discuss the use of speaker transformations in the training process