Non-linear dimension reduction of Gabor features for noise-robust ASR

It has been shown that Gabor filters closely resemble the spectro-temporal response fields of neurons in the primary auditory cortex. A filter bank of 2-D Gabor filters can be applied to either the mel-spectrogram or power normalized spectrogram to obtain a set of physiologically inspired Gabor Filter Bank Features. The high dimensionality and the correlated nature of these features pose an issue for ASR. In the past, dimension reduction was performed through (1) feature selection, (2) channel selection, (3) linear dimension reduction or (4) tandem acoustic modelling. In this paper, we propose a novel solution to this issue based on channel selection and non-linear dimension reduction using Laplacian Eigenmaps. These features are concatenated with Power Normalized Cepstral Coefficients (PNCC) to evaluate if the two are complementary and provide an improvement in performance. We show a relative reduction of 12.66% in the WER compared to the PNCC baseline, when applied to the Aurora 4 database.