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Peter Sullivan

Peter Sullivan has 3 papers in our library, in 2021–2023, most of them on Speech & spoken language, English, Language models & pretraining; often with Muhammad Abdul-Mageed, Toshiko Shibano, AbdelRahim Elmadany.

3 papers in our library · 2021–2023

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📄 3 works in our library · 🔗 Cited 17 times in our library · ⭐ Paper Stars ranking: #5478

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksSignal Processing · 3 worksPsychology · 2 worksExperimental and Cognitive Psychology · 2 works

🧭 Topics 4Research subjects and languages the work is about.Semitic languages · 1 workEnglish · 2 worksSpeech & spoken language · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20231 paperPeter Sullivan's papers of 202320221 paperPeter Sullivan's papers of 202220211 paperPeter Sullivan's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. On the Robustness of Arabic Speech Dialect Identification (2023 · 18 citations)
    Arabic dialect identification (ADI) systems often act as filters in large web-scale pipelines that gather speech for training ASR.
  2. Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decoding (2022 · 16 citations)
    ASR systems tuned for native (L1) English perform noticeably worse on non-native (L2) speakers. Accents shift pronunciation, and little annotated accented audio exists for training. The authors start from the self-trained wav2vec 2.0 Large model (Wav2Vec 2.0-ST) and fine-tune it with CTC under many data conditions…
  3. Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning (2021 · 5 citations)
    English speech recognisers do noticeably worse on non-native (L2) speakers. The main causes are first-language pronunciation habits and the small amount of transcribed accented speech. The authors test whether a strong pretrained model can close this gap through modest fine-tuning alone, with no external language…

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 17 times (of the 141,840 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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