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We present a state-of-the-art speech recognition system developed usingend-to-end deep learning. Our architecture is significantly simpler thantraditional speech systems, which rely on laboriously engineered processingpipelines; these traditional systems also tend to perform poorly when used innoisy environments. In contrast, our system does not need hand-designedcomponents to model background noise, reverberation, or speaker variation, butinstead directly learns a function that is robust to such effects. We do notneed a phoneme dictionary, nor even the concept of a "phoneme." Key to ourapproach is a well-optimized RNN training system that uses multiple GPUs, aswell as a set of novel data synthesis techniques that allow us to efficientlyobtain a large amount of varied data for training. Our system, called DeepSpeech, outperforms previously published results on the widely studiedSwitchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speechalso handles challenging noisy environments better than widely used,state-of-the-art commercial speech systems.

Deep Speech: Scaling up end-to-end speech recognition