Deployed end-to-end speech recognition

A computer-implemented method for transcribing speech comprising:

• receiving an input audio from a user, the input audio comprising an utterance;
• generating a set of spectrogram frames for the utterance;
• inputting the set of spectrogram frames into a deep neural network (DNN) model, the DNN model comprising one or more convolution layers, one or more recurrent layers, and a row convolution layer in which an activation at a current time step of the row convolution layer is obtained using information from at least one of the one or more recurrent layers at the current time step and at least one future time step, the DNN model having been trained using a plurality of minibatches of training utterance sequences and batch normalization to normalize pre-activations in at least one of the one or more recurrent layers during training;
• obtaining probabilities for one or more predicted characters from the DNN model;
• and
• performing a search to obtain a transcription of the utterance using at least some of the probabilities of the one or more predicted characters and a language model.