In this paper, we present new theory and results that combine constrained Maximum Likelihood Linear Regression (MLLR), known as feature space MLLR (fMLLR), a state-of-the-art model adaptation technique, with Dynamic Noise Adaptation (DNA), a state-of-the-art noise adaptation algorithm. We explain how DNA implements a highly non-linear transform on speech model features, and why DNA is better suited for compensating for additive noise than fMLLR. Tests results are presented on the DNA + Aurora II framework, which is based upon a collection of challenging in-car noise recordings, as a function of SNR. The results demonstrate that DNA significantly outperforms block fMLLR on additive noise, and that DNA + fMLLR outperforms the ETSI advanced front-end (AFE) system + fMLLR by a significant margin (over 7% absolute).
Bibliographic reference. Rennie, Steven J. / Dognin, Pierre L. (2008): "Beyond linear transforms: efficient non-linear dynamic adaptation for noise robust speech recognition", In INTERSPEECH-2008, 1305-1308.