UserLibri: A Dataset for ASR Personalization Using Only Text

Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani, Shefali Garg, Rajiv Mathews +4 more
7/2/2022
eess.AScs.CLcs.LG

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Abstract

Personalization of speech models on mobile devices (on-device personalization) is an active area of research, but more often than not, mobile devices have more text-only data than paired audio-text data. We explore training a personalized language model on text-only data, used during inference to improve speech recognition performance for that user. We experiment on a user-clustered LibriSpeech corpus, supplemented with personalized text-only data for each user from Project Gutenberg. We release this User-Specific LibriSpeech (UserLibri) dataset to aid future personalization research. LibriSpeech audio-transcript pairs are grouped into 55 users from the test-clean dataset and 52 users from test-other. We are able to lower the average word error rate per user across both sets in streaming and nonstreaming models, including an improvement of 2.5 for the harder set of test-other users when streaming.

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Cite this paper

@article{breiner2022userlibri,
  title  = {UserLibri: A Dataset for ASR Personalization Using Only Text},
  author = {Theresa Breiner and Swaroop Ramaswamy and Ehsan Variani and Shefali Garg and Rajiv Mathews and Khe Chai Sim and Kilol Gupta and Mingqing Chen and Lara McConnaughey},
  year   = {2022},
  eprint = {2207.00706},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.00706v1}
}

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