  This paper presents a simple end-to-end model for speech recognition,
combining a convolutional network based acoustic model and a graph decoding. It
is trained to output letters, with transcribed speech, without the need for
force alignment of phonemes. We introduce an automatic segmentation criterion
for training from sequence annotation without alignment that is on par with CTC
while being simpler. We show competitive results in word error rate on the
Librispeech corpus with MFCC features, and promising results from raw waveform.
