The IBM 2016 English Conversational Telephone Speech Recognition System

George Saon, Tom Sercu, Steven Rennie, Hong-Kwang J. Kuo

2016-04-27Speech Recognitionspeech-recognitionLanguage Modelling
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Abstract

We describe a collection of acoustic and language modeling techniques that lowered the word error rate of our English conversational telephone LVCSR system to a record 6.6% on the Switchboard subset of the Hub5 2000 evaluation testset. On the acoustic side, we use a score fusion of three strong models: recurrent nets with maxout activations, very deep convolutional nets with 3x3 kernels, and bidirectional long short-term memory nets which operate on FMLLR and i-vector features. On the language modeling side, we use an updated model "M" and hierarchical neural network LMs.

Results

TaskDatasetMetricValueModel
Speech Recognitionswb_hub_500 WER fullSWBCHPercentage error12.2RNN + VGG + LSTM acoustic model trained on SWB+Fisher+CH, N-gram + "model M" + NNLM language model
Speech RecognitionSwitchboard + Hub500Percentage error6.6RNN + VGG + LSTM acoustic model trained on SWB+Fisher+CH, N-gram + "model M" + NNLM language model
Speech RecognitionSwitchboard + Hub500Percentage error6.9IBM 2016

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