Papers › The IBM 2016 English Conversational Telephone Speech Recognition System
The IBM 2016 English Conversational Telephone Speech Recognition System
George Saon, Tom Sercu, Steven Rennie, Hong-Kwang J. Kuo
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Recognition | Switchboard + Hub500 | RNN + VGG + LSTM acoustic model trained on SWB+Fisher+CH, N-gram + "model M" + NNLM language model | Percentage error | 6.6 | #7 of 30 | Archive leaderboard | report |
| Speech Recognition | Switchboard + Hub500 | IBM 2016 | Percentage error | 6.9 | #9 of 30 | Archive leaderboard | report |
| Speech Recognition | swb_hub_500 WER fullSWBCH | RNN + VGG + LSTM acoustic model trained on SWB+Fisher+CH, N-gram + "model M" + NNLM language model | Percentage error | 12.2 | #5 of 12 | Archive leaderboard | report |
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Methods
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