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Jasper: An End-to-End Convolutional Neural Acoustic Model

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arxiv 1904.03288 v3 pith:6VKQKA67 submitted 2019-04-05 eess.AS cs.CLcs.LGcs.SD

Jason Li , Vitaly Lavrukhin , Boris Ginsburg , Ryan Leary , Oleksii Kuchaiev , Jonathan M. Cohen , Huyen Nguyen , Ravi Teja Gadde This is my paper · ORCID
classification eess.AScs.CLcs.LGcs.SD
keywords jaspermodelarchitectureconvolutionaldecoderend-to-endexternallibrispeech
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data. Our model, Jasper, uses only 1D convolutions, batch normalization, ReLU, dropout, and residual connections. To improve training, we further introduce a new layer-wise optimizer called NovoGrad. Through experiments, we demonstrate that the proposed deep architecture performs as well or better than more complex choices. Our deepest Jasper variant uses 54 convolutional layers. With this architecture, we achieve 2.95% WER using a beam-search decoder with an external neural language model and 3.86% WER with a greedy decoder on LibriSpeech test-clean. We also report competitive results on the Wall Street Journal and the Hub5'00 conversational evaluation datasets.

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