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The IBM 2015 English Conversational Telephone Speech Recognition System
lib:4d8bf3030d142d17 (v1.0.0)
Authors: George Saon,Hong-Kwang J. Kuo,Steven Rennie,Michael Picheny
ArXiv: 1505.05899
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1505.05899v1
We describe the latest improvements to the IBM English conversational telephone speech recognition system. Some of the techniques that were found beneficial are: maxout networks with annealed dropout rates; networks with a very large number of outputs trained on 2000 hours of data; joint modeling of partially unfolded recurrent neural networks and convolutional nets by combining the bottleneck and output layers and retraining the resulting model; and lastly, sophisticated language model rescoring with exponential and neural network LMs. These techniques result in an 8.0% word error rate on the Switchboard part of the Hub5-2000 evaluation test set which is 23% relative better than our previous best published result.
ArXiv: 1505.05899
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1505.05899v1
We describe the latest improvements to the IBM English conversational telephone speech recognition system. Some of the techniques that were found beneficial are: maxout networks with annealed dropout rates; networks with a very large number of outputs trained on 2000 hours of data; joint modeling of partially unfolded recurrent neural networks and convolutional nets by combining the bottleneck and output layers and retraining the resulting model; and lastly, sophisticated language model rescoring with exponential and neural network LMs. These techniques result in an 8.0% word error rate on the Switchboard part of the Hub5-2000 evaluation test set which is 23% relative better than our previous best published result.
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