This paper presents Deep Speech 2, an end-to-end deep learning system for automatic speech recognition that works across vastly different languages (English and Mandarin). It replaces traditional hand-engineered ASR pipelines with neural networks, achieving human-competitive transcription accuracy on standard datasets. The system uses HPC techniques for 7x speedup, enabling faster experimentation. Key innovations include Batch Normalization for RNNs, curriculum learning (SortaGrad), and GPU deployment optimization (Batch Dispatch). The approach demonstrates that end-to-end learning can handle diverse speech conditions including noise, accents, and different languages, representing a significant step toward universal speech recognition systems.
