  In this work we present simple grapheme-based system for low-resource speech
recognition using Babel data for Turkish spontaneous speech (80 hours). We have
investigated different neural network architectures performance, including
fully-convolutional, recurrent and ResNet with GRU. Different features and
normalization techniques are compared as well. We also proposed CTC-loss
modification using segmentation during training, which leads to improvement
while decoding with small beam size. Our best model achieved word error rate of
45.8%, which is the best reported result for end-to-end systems using in-domain
data for this task, according to our knowledge.
