  We study large-scale kernel methods for acoustic modeling in speech
recognition and compare their performance to deep neural networks (DNNs). We
perform experiments on four speech recognition datasets, including the TIMIT
and Broadcast News benchmark tasks, and compare these two types of models on
frame-level performance metrics (accuracy, cross-entropy), as well as on
recognition metrics (word/character error rate). In order to scale kernel
methods to these large datasets, we use the random Fourier feature method of
Rahimi and Recht (2007). We propose two novel techniques for improving the
performance of kernel acoustic models. First, in order to reduce the number of
random features required by kernel models, we propose a simple but effective
method for feature selection. The method is able to explore a large number of
non-linear features while maintaining a compact model more efficiently than
existing approaches. Second, we present a number of frame-level metrics which
correlate very strongly with recognition performance when computed on the
heldout set; we take advantage of these correlations by monitoring these
metrics during training in order to decide when to stop learning. This
technique can noticeably improve the recognition performance of both DNN and
kernel models, while narrowing the gap between them. Additionally, we show that
the linear bottleneck method of Sainath et al. (2013) improves the performance
of our kernel models significantly, in addition to speeding up training and
making the models more compact. Together, these three methods dramatically
improve the performance of kernel acoustic models, making their performance
comparable to DNNs on the tasks we explored.
