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      Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

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          Abstract

          We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit fairseq. Espresso supports distributed training across GPUs and computing nodes, and features various decoding approaches commonly employed in ASR, including look-ahead word-based language model fusion, for which a fast, parallelized decoder is implemented. Espresso achieves state-of-the-art ASR performance on the WSJ, LibriSpeech, and Switchboard data sets among other end-to-end systems without data augmentation, and is 4--11x faster for decoding than similar systems (e.g. ESPnet).

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          Most cited references 10

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          Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI

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            Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition

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              Towards Better Decoding and Language Model Integration in Sequence to Sequence Models

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                Author and article information

                Journal
                18 September 2019
                Article
                1909.08723

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Accepted to ASRU 2019
                cs.CL cs.SD eess.AS

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