Introducing Temporal Asymmetries in Feature Extraction for Automatic Speech Recognition

We propose a new auditory inspired feature extraction technique for automatic speech recognition (ASR). Features are extracted by filtering the temporal trajectory of spectral energies in each critical band of speech by a bank of finite impulse response (FIR) filters. Impulse responses of these filters are derived from a modified Gabor envelope in order to emulate asymmetries of the temporal receptive field (TRF) profiles observed in higher level auditory neurons. We obtain $11.4\%$ relative improvement in word error rate on OGI-Digits database and, $3.2\%$ relative improvement in phoneme error rate on TIMIT database over the MRASTA technique

Classification of integrable Weingarten surfaces possessing an sl(2)-valued zero curvature representation

In this paper we classify Weingarten surfaces integrable in the sense of soliton theory. The criterion is that the associated Gauss equation possesses an sl(2)-valued zero curvature representation with a nonremovable parameter. Under certain restrictions on the jet order, the answer is given by a third order ordinary differential equation to govern the functional dependence of the principal curvatures. Employing the scaling and translation (offsetting) symmetry, we give a general solution of the governing equation in terms of elliptic integrals. We show that the instances when the elliptic integrals degenerate to elementary functions were known to nineteenth century geometers. Finally, we characterize the associated normal congruences

Reverse Correlation for analyzing MLP Posterior Features in ASR

In this work, we investigate the reverse correlation technique for analyzing posterior feature extraction using an multilayered perceptron trained on multi-resolution RASTA (MRASTA) features. The filter bank in MRASTA feature extraction is motivated by human auditory modeling. The MLP is trained based on an error criterion and is purely data driven. In this work, we analyze the functionality of the combined system using reverse correlation analysis

Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

We present a framework to apply Volterra series to analyze multilayered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. The identified Volterra kernels reveal the spectro-temporal patterns that are learned by the trained system for each phoneme. To demonstrate the applicability of Volterra series, we analyze a multilayered perceptron trained using Mel filter bank energy features and analyze its first order Volterra kernels