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In this paper, we report state-of-the-art results on LibriSpeech amongend-to-end speech recognition models without any external training data. Ourmodel, Jasper, uses only 1D convolutions, batch normalization, ReLU, dropout,and residual connections. To improve training, we further introduce a newlayer-wise optimizer called NovoGrad. Through experiments, we demonstrate thatthe proposed deep architecture performs as well or better than more complexchoices. Our deepest Jasper variant uses 54 convolutional layers. With thisarchitecture, we achieve 2.95% WER using a beam-search decoder with an externalneural language model and 3.86% WER with a greedy decoder on LibriSpeechtest-clean. We also report competitive results on the Wall Street Journal andthe Hub5'00 conversational evaluation datasets.

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