Recent Advances in End-to-End Automatic Speech Recognition

Recently, the speech community is seeing a significant trend of moving from deep neural network based hybrid modeling to end-to-end (E2E) for automatic recognition (ASR). While E2E models achieve state-of-the-art results in most benchmarks terms ASR accuracy, are still used large proportion commercial systems at current time. There lots practical factors that affect production model deployment decision. Traditional models, being optimized decades, usually good these factors. Without providing excellent solutions all factors, it hard be widely commercialized. In this paper, we will overview recent advances focusing on technologies addressing those challenges industry's perspective.