A Comparison Between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition

Zhiyun Lu 1 Dong Guo 2 Alireza Bagheri Garakani 2 Kuan Liu 2 Avner May 3 Aurélien Bellet 4 Linxi Fan 2 Michael Collins 3 Brian Kingsbury 5 Michael Picheny 5 Fei Sha 1
1 UCLA - University of California [Los Angeles]
2 USC - University of Southern California
3 Columbia University [New York]
4 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
5 IBM Thomas J. Watson Research Center
Abstract : We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as effective as their DNN counterparts. However, on token-error-rates DNN models can be significantly better. We have discovered that this might be attributed to DNN's unique strength in reducing both the perplexity and the entropy of the predicted posterior probabilities. Motivated by our findings, we propose a new technique, entropy regularized perplexity, for model selection. This technique can noticeably improve the recognition performance of both types of models, and reduces the gap between them. While effective on Broadcast News, this technique could be also applicable to other tasks.
Keywords : deep neural networks kernel methods acoustic models automatic speech recognition
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Conference papers
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Contributor : Aurélien Bellet <>
Submitted on : Friday, June 10, 2016 - 5:20:53 PM
Last modification on : Tuesday, September 10, 2019 - 11:32:08 AM

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  • HAL Id : hal-01329772, version 1
  • ARXIV : 1603.05800

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Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, et al.. A Comparison Between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Mar 2016, Shanghai, China. ⟨hal-01329772⟩

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