Noise robust model adaptation using linear spline interpolation

This paper presents a novel data-driven technique for performing acoustic model adaptation to noisy environments. In the presence of additive noise, the relationship between log mel spectra of speech, noise and noisy speech is nonlinear. Traditional methods linearize this relationship using the mode of the nonlinearity or use some other approximation. The approach presented in this paper models this nonlinear relationship using linear spline regression. In this method, the set of spline parameters that minimizes the error between the predicted and actual noisy speech features is learned from training data, and used at runtime to adapt clean acoustic model parameters to the current noise conditions. Experiments were performed to evaluate the performance of the system on the Aurora 2 task. Results show that the proposed adaptation algorithm (word accuracy 89.22%) outperforms VTS model adaptation (word accuracy 88.38%).