arXiv:2001.07263eess.AS
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard
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Abstract
It is generally believed that direct sequence-to-sequence (seq2seq) speech recognition models are competitive with hybrid models only when a large amount of data, at least a thousand hours, is available for training. In this paper, we show that state-of-the-art recognition performance can be achieved on the Switchboard-300 database using a single headed attention, LSTM based model. Using a cross-utterance language model, our single-pass speaker independent system reaches 6.4% and 12.5% word error rate (WER) on the Switchboard and CallHome subsets of Hub5'00, without a pronunciation lexicon. While careful regularization and data augmentation are crucial in achieving this level of performance, experiments on Switchboard-2000 show that nothing is more useful than more data. Overall, the combination of various regularizations and a simple but fairly large model results in a new state of the art, 4.7% and 7.8% WER on the Switchboard and CallHome sets, using SWB-2000 without any external data resources.
This (https://arxiv.org/abs/2010.03449) is by far not the first publication which claims super human speech recognition, or human parity. For example https://arxiv.org/abs/1610.05256 or https://arxiv.org/abs/1703.02136. And since then, the WER has dropped much more on the same…
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