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Link to original content: https://doi.org/10.48550/arXiv.2207.00706
[2207.00706] UserLibri: A Dataset for ASR Personalization Using Only Text
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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2207.00706 (eess)
[Submitted on 2 Jul 2022]

Title:UserLibri: A Dataset for ASR Personalization Using Only Text

Authors:Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani, Shefali Garg, Rajiv Mathews, Khe Chai Sim, Kilol Gupta, Mingqing Chen, Lara McConnaughey
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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.
Comments: Accepted for publication in Interspeech 2022. 9 total pages with appendix, 9 total tables, 5 total figures
Subjects: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2207.00706 [eess.AS]
  (or arXiv:2207.00706v1 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2207.00706

Submission history

From: Theresa Breiner [view email]
[v1] Sat, 2 Jul 2022 01:03:01 UTC (201 KB)
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