Jacobian approach to fast acoustic model adaptation

This paper describes a Jacobian approach to fast adaptation of acoustic models to noisy environments. Acoustic models under a noise assumption are compensated by Jacobian matrices with the difference between assumed and observed noise cepstra. Detailed mathematical formulation and algorithm derivation are presented. Experiments showed that when a small amount of training data is given, this approach outperforms the existing approaches (such as PMC and NOVO) for composing a model from speech and noise models. It drastically reduces computational cost by replacing the complicated computation of model composition by simple matrix arithmetic and enables real-time environmental noise adaptation. Combination with spectrum subtraction is also discussed