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It is generally believed that direct sequence-to-sequence (seq2seq) speechrecognition models are competitive with hybrid models only when a large amountof 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 theSwitchboard-300 database using a single headed attention, LSTM based model.Using a cross-utterance language model, our single-pass speaker independentsystem reaches 6.4% and 12.5% word error rate (WER) on the Switchboard andCallHome subsets of Hub5'00, without a pronunciation lexicon. While carefulregularization and data augmentation are crucial in achieving this level ofperformance, experiments on Switchboard-2000 show that nothing is more usefulthan more data. Overall, the combination of various regularizations and asimple 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 externaldata resources.

Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard