  We propose a new end-to-end neural acoustic model for automatic speech
recognition. The model is composed of multiple blocks with residual connections
between them. Each block consists of one or more modules with 1D time-channel
separable convolutional layers, batch normalization, and ReLU layers. It is
trained with CTC loss. The proposed network achieves near state-of-the-art
accuracy on LibriSpeech and Wall Street Journal, while having fewer parameters
than all competing models. We also demonstrate that this model can be
effectively fine-tuned on new datasets.
