In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 h). 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.
We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a "phoneme." Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0 error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.  A developing material for use in the development of electrostatic images comprising toner particles and carrier particles is described wherein the developing material incorporates at least one solid hydrophobic bivalent or trivalent met al salt of a half-ester of a branched-chain or straight-chain aliphatic dicarboxylic acid and or at least one bivalent or trivalent met al salt of a monoester or diester of a phosphorus oxyacid. The images obtained by means of this developing material have an improved image contrast, solid area coverage and surface smoothness. The toner particles show less tendency to cake and agglomerate and have improved mechanical strength. Cleaning of the imaging surface between use and re-use is also facilitated.