Arccosine kernels: Acoustic modeling with infinite neural networks

Neural networks are a useful alternative to Gaussian mixture models for acoustic modeling; however, training multilayer networks involves a difficult, nonconvex optimization that requires some “art” to make work well in practice. In this paper we investigate the use of arccosine kernels for speech recognition, using these kernels in a hybrid support vector machine/hidden Markov model recognition system. Arccosine kernels approximate the computation in a certain class of infinite neural networks using a single kernel function, but can be used in learners that require only a convex optimization for training. Phone recognition experiments on the TIMIT corpus show that arccosine kernels can outperform radial basis function kernels.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Chih-Chieh Cheng, Brian Kingsbury
• Keywords: Basis Function Kernels, ICASSP 2011, Markov Model Recognition, Neural Networks, Signal Processing