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arXiv:1811.06621cs.CL

Streaming End-to-end Speech Recognition For Mobile Devices

Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar, Ian McGraw, Raziel Alvarez, Ding Zhao, David Rybach, Anjuli Kannan, Yonghui Wu, Ruoming Pang, Qiao Liang, Deepti Bhatia, Yuan Shangguan, Bo Li, Golan Pundak, Khe Chai Sim, Tom Bagby, Shuo-yiin Chang, Kanishka Rao, Alexander Gruenstein

Abstract

End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.

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Subjects
cs.CLComputation and Language

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Linked by 4 identifiable commenters in 5 comments across 5 threads, from Mar 2019 to Nov 2020.

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Discussed in comments5 threads

  1. Show HN: LibreASR – An On-Premises, Streaming Speech Recognition System 1 comment

    • Hey HN! I've been working on this for a while now. While there are other on-premise solutions using older models such as DeepSpeech [0], I haven't found a deployable project supporting multiple languages using the recent RNN-T Architecture [1]. Please note that this does not…

      iceychrisView on HNParent context

  2. Show HN: Open-source online speech recognition 1 comment

    • Online decoding in Kaldi uses GMM (or NN+GMM hybrid) where this repo focus in end to end approach using deep LSTM (similar to Google Pixel's offline speech to text [1]). [1] https://arxiv.org/pdf/1811.06621.pdf

      polymorph1smView on HNParent context

  3. Bringing Google Live Transcribe's Speech Engine to Everyone 1 comment

    • Google published research [1][2] on offline recognition and it was rolled out earlier in 2019. Model size for English is claimed to be under 100MB, https://techcrunch.com/2019/03/12/googles-new-voice-recognit... [1] https://arxiv.org/pdf/1603.03185.pdf (2016) [2]…

      walterbellView on HNParent context

  4. Federated Learning 1 comment

    • Google mentioned at I/O that speech recognition will soon (this summer?) be performed locally on Android devices, with no voice data being sent to Google, because they have been able to reduce the size of the model dramatically. Is that related to federated learning? Paper…

      walterbellView on HNParent context

  5. Google’s new voice recognition system works instantly and offline (Pixel only) 1 comment

    • Google AI blog: https://ai.googleblog.com/2019/03/an-all-neural-on-device-sp... arXiv: https://arxiv.org/abs/1811.06621

      modelessView on HNParent context

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