Filter-bank optimization for Frequency Domain Linear Prediction

The sub-band Frequency Domain Linear Prediction (FDLP) technique estimates autoregressive models of Hilbert envelopes of subband signals, from segments of discrete cosine transform (DCT) of a speech signal, using windows. Shapes of the windows and their positions on the cosine transform of the signal determine implied filtering of the signal. Thus, the choices of shape, position and number of these windows can be critical for the performance of the FDLP technique. So far, we have used Gaussian or rectangular windows. In this paper asymmetric cochlear-like filters are being studied. Further, a frequency differentiation operation, that introduces an additional set of parameters describing local spectral slope in each frequency sub-band, is introduced to increase the robustness of sub-band envelopes in noise. The performance gains achieved by these changes are reported in a variety of additive noise conditions, with an average relative improvement of 8.04% in phoneme recognition accuracy.