  We investigate the potential of stochastic neural networks for learning
effective waveform-based acoustic models. The waveform-based setting, inherent
to fully end-to-end speech recognition systems, is motivated by several
comparative studies of automatic and human speech recognition that associate
standard non-adaptive feature extraction techniques with information loss which
can adversely affect robustness. Stochastic neural networks, on the other hand,
are a class of models capable of incorporating rich regularization mechanisms
into the learning process. We consider a deep convolutional neural network that
first decomposes speech into frequency sub-bands via an adaptive parametric
convolutional block where filters are specified by cosine modulations of
compactly supported windows. The network then employs standard non-parametric
1D convolutions to extract relevant spectro-temporal patterns while gradually
compressing the structured high dimensional representation generated by the
parametric block. We rely on a probabilistic parametrization of the proposed
neural architecture and learn the model using stochastic variational inference.
This requires evaluation of an analytically intractable integral defining the
Kullback-Leibler divergence term responsible for regularization, for which we
propose an effective approximation based on the Gauss-Hermite quadrature. Our
empirical results demonstrate a superior performance of the proposed approach
over comparable waveform-based baselines and indicate that it could lead to
robustness. Moreover, the approach outperforms a recently proposed deep
convolutional neural network for learning of robust acoustic models with
standard FBANK features.
