PCA-PMC: a novel use of a priori knowledge for fast parallel model combination

TLDR: The novel approach here is to encode the clean models using principal component analysis (PCA) and pre-compute the prototype vectors and matrices for the means and covariances in the linear spectral-domain using rectangular DCT and inverse DCT matrices.

Abstract: This paper describes an algorithm to reduce computational complexity of the parallel model combination (PMC) method for robust speech recognition while retaining the same level of performance. Although, PMC is effective in composing a noise corrupted acoustic model from clean speech and noise models, the intense computational complexity limits its use in real-time use. The novel approach here is to encode the clean models using principal component analysis (PCA) and pre-compute the prototype vectors and matrices for the means and covariances in the linear spectral-domain using rectangular DCT and inverse DCT matrices. Therefore, transformation into the linear spectral domain is reduced to finding the projection of each vector in the eigen space of means and covariances followed by a linear combination of vectors and matrices obtained from the projections. Furthermore, the eigen space allows a better trade-off for reducing computational complexity versus accuracy. The computational savings are demonstrated both analytically and through experimental evaluations. Experiments using context independent phone recognition with TIMIT data shows that the new PMC framework can outperforms the baseline method by a factor of 1.9 with the same level of accuracy.