Kernel Approximation Methods for Speech Recognition
Abstract
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. (2013a) 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.
Research Areas
