% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@inproceedings{whisper_l_v3_paper,
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
title = {Robust speech recognition via large-scale weak supervision},
year = {2023},
publisher = {JMLR.org},
abstract = {We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results without the need for any dataset specific fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
articleno = {1182},
numpages = {27},
location = {Honolulu, Hawaii, USA},
series = {ICML'23}
}

@inproceedings{xls_r_paper,
  title     = {{XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale}},
  author    = {Arun Babu and Changhan Wang and Andros Tjandra and Kushal Lakhotia and Qiantong Xu and Naman Goyal and Kritika Singh and Patrick {von Platen} and Yatharth Saraf and Juan Pino and Alexei Baevski and Alexis Conneau and Michael Auli},
  year      = {2022},
  booktitle = {{Interspeech 2022}},
  pages     = {2278--2282},
  doi       = {10.21437/Interspeech.2022-143},
  issn      = {2958-1796},
}

@article{mms_paper,
  author  = {Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
  title   = {Scaling Speech Technology to 1,000+ Languages},
  journal = {Journal of Machine Learning Research},
  year    = {2024},
  volume  = {25},
  number  = {97},
  pages   = {1--52},
  url     = {http://jmlr.org/papers/v25/23-1318.html}
}

@inproceedings{wav_2_vec_2_paper,
author = {Baevski, Alexei and Zhou, Henry and Mohamed, Abdelrahman and Auli, Michael},
title = {wav2vec 2.0: a framework for self-supervised learning of speech representations},
year = {2020},
isbn = {9781713829546},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.},
booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
articleno = {1044},
numpages = {12},
location = {Vancouver, BC, Canada},
series = {NIPS '20}
}

@InProceedings{peft_paper,
  title = 	 {Parameter-Efficient Transfer Learning for {NLP}},
  author =       {Houlsby, Neil and Giurgiu, Andrei and Jastrzebski, Stanislaw and Morrone, Bruna and De Laroussilhe, Quentin and Gesmundo, Andrea and Attariyan, Mona and Gelly, Sylvain},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {2790--2799},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/houlsby19a/houlsby19a.pdf},
  url = 	 {https://proceedings.mlr.press/v97/houlsby19a.html},
  abstract = 	 {Fine-tuning large pretrained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter’s effectiveness, we transfer the recently proposed BERT Transformer model to $26$ diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within $0.8%$ of the performance of full fine-tuning, adding only $3.6%$ parameters per task. By contrast, fine-tuning trains $100%$ of the parameters per task.}
}

@misc{mms_hf_aft_blog,
    author = {Patrick von Platen},
    title = {Fine-tuning MMS Adapter Models for Multi-Lingual ASR},
    howpublished = {\url{https://huggingface.co/blog/mms_adapters}},
    year = {2023},
    note = {[Blog post; Accessed on 11 March 2026]}
}
}

@misc{xlsr_hf_aft_blog,
    author = {Patrick von Platen},
    title = {Fine-tuning XLS-R for Multi-Lingual ASR with Transformers},
    howpublished = {\url{https://huggingface.co/blog/fine-tune-xlsr-wav2vec2}},
    year = {2021},
    note = {[Blog post; Accessed on 11 March 2026]}
}
}

@misc{whisper_hf_aft_blog,
    author = {Sanchit Gandhi},
    title = {Fine-Tune Whisper For Multilingual ASR with Transformerss},
    howpublished = {\url{https://huggingface.co/blog/fine-tune-whisper}},
    year = {2022},
    note = {[Blog post; Accessed on 11 March 2026]}
}

@Inbook{Anderson2007,
author="Anderson, Jean
and Beavan, Dave
and Kay, Christian",
editor="Beal, Joan C.
and Corrigan, Karen P.
and Moisl, Hermann L.",
title="SCOTS: Scottish Corpus of Texts and Speech",
bookTitle="Creating and Digitizing Language Corpora: Volume 1: Synchronic Databases",
year="2007",
publisher="Palgrave Macmillan UK",
address="London",
pages="17--34",
abstract="Scotland contains a rich variety of languages. A recent survey of 300 respondents revealed that over 30 different languages are regularly spoken at home and at work (Institute for the Languages of Scotland (ILS) Report, 2003). In addition to the Scottish English spoken by the majority, there are substantial numbers of speakers of the other indigenous languages, with an estimated 58,650 Scottish Gaelic speakers at the 2001 Census, and about half the population claiming knowledge of Scots (Macafee, 2000).1 Non-indigenous languages include Arabic, Bengali, Cantonese, Dutch, Hindi, Italian, Kurdish, Polish, Panjabi, Romany and Urdu. British Sign Language has a flourishing Scottish variety.",
isbn="978-0-230-22393-6",
doi="10.1057/9780230223936_2",
url="https://doi.org/10.1057/9780230223936_2"
}

@article{douglas_scot_problems,
    author = {Douglas, Fiona M.},
    title = {The Scottish Corpus of Texts and Speech: Problems of Corpus Design},
    journal = {Literary and Linguistic Computing},
    volume = {18},
    number = {1},
    pages = {23-37},
    year = {2003},
    month = {04},
    abstract = {In recent years, the use of large corpora has revolutionized the way we study language. There are now numerous well‐established corpus projects, which have set the standard for future corpus‐based research. As more and more corpora are developed and technology continues to offer greater and greater scope, the emphasis has shifted from corpus size to establishing norms of good practice. There is also an increasingly critical appreciation of the crucial role played by corpus design. Corpus design can, however, present peculiar problems for particular types of source material. The Scottish Corpus of Texts and Speech (SCOTS) is the first large‐scale corpus project specifically dedicated to the languages of Scotland, and therefore it faces many unanswered questions, which will have a direct impact on the corpus design. The first phase of the project will focus on the language varieties Scots and Scottish English, varieties that are themselves notoriously difficult to define. This paper outlines the complexities of the Scottish linguistic situation, before going on to examine the problematic issue of how to construct a well‐balanced and representative corpus in what is largely uncharted territory. It argues that a well‐formed corpus cannot be constructed in a linguistic vacuum, and that familiarity with the overall language population is essential before effective corpus sampling techniques, methodologies, and categorization schema can be devised. It also offers some preliminary methodologies that will be adopted by SCOTS.},
    issn = {0268-1145},
    doi = {10.1093/llc/18.1.23},
    url = {https://doi.org/10.1093/llc/18.1.23},
    eprint = {https://academic.oup.com/dsh/article-pdf/18/1/23/6376478/180023.pdf},
}

@misc{rafkin2026taskarithmeticsupportlanguages,
      title={Task Arithmetic with Support Languages for Low-Resource ASR}, 
      author={Emma Rafkin and Dan DeGenaro and Xiulin Yang},
      year={2026},
      eprint={2601.07038},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.07038}, 
}

@incollection{scots_spade,
    author = {Sonderegger, Morgan and Stuart-Smith, Jane and McAuliffe, Michael and Macdonald, Rachel and Kendall, Tyler},
    isbn = {9780262366076},
    title = {Managing Data for Integrated Speech Corpus Analysis in SPeech Across Dialects of English (SPADE)},
    booktitle = {The Open Handbook of Linguistic Data Management},
    publisher = {The MIT Press},
    year = {2022},
    month = {01},
    doi = {10.7551/mitpress/12200.003.0020},
    url = {https://doi.org/10.7551/mitpress/12200.003.0020},
    eprint = {https://direct.mit.edu/book/chapter-pdf/2249652/c013400_9780262366076.pdf},
}


@misc{scots_syntax_atlas,
    author = {Smith, Jennifer and Adger, David and Aitken, Brian and Heycock, Caroline and Jamieson, E and Thoms, Gary},
    title = {The Scots Syntax Atlas},
    publisher = {University of Glasgow},
    howpublished = {\url{https://scotssyntaxatlas.ac.uk}},
    year = {2019},
    note = {[Accessed on 16 March 2026]}
}

@article{adger2023intuitions,
  title={‘When intuitions (don't) fail’: combining syntax and sociolinguistics in the analysis of Scots},
  author={Adger, D and Jamieson, E and Smith, J and Thoms, G and Heycock, C},
  journal={English Language \& Linguistics},
  year={2023},
  publisher={Cambridge University Press}
}

@Inbook{Anderwald2007,
author="Anderwald, Lieselotte
and Wagner, Susanne",
editor="Beal, Joan C.
and Corrigan, Karen P.
and Moisl, Hermann L.",
title="FRED --- The Freiburg English Dialect Corpus: Applying Corpus-Linguistic Research Tools to the Analysis of Dialect Data",
bookTitle="Creating and Digitizing Language Corpora: Volume 1: Synchronic Databases",
year="2007",
publisher="Palgrave Macmillan UK",
address="London",
pages="35--53",
abstract="When, after almost two years of preliminary research, the actual compilation of FRED started in March 2000, the Freiburg team had had enough time to think about, discuss, consider possible and dismiss impossible research questions and devise a list of objectives for which the finished corpus should be suitable.1 The research tradition of other Freiburg corpora provided two clear objectives (for F-LOB and Frown, see, for example, Mair, 2002; Mair et al., 2002).",
isbn="978-0-230-22393-6",
doi="10.1057/9780230223936_3",
url="https://doi.org/10.1057/9780230223936_3"
}

@inbook{MoChridhe_2020, edition={1st Edition}, title={Digital humanities and critical engagement: The case of the Scottish Corpus of Texts & Speech and Wee Windaes}, url={https://doi.org/10.4324/9780429399923-5}, DOI={10.4324/9780429399923-5}, booktitle={Transformative Digital Humanities}, publisher={Routledge}, author={MoChridhe, Race}, year={2020}, month=apr, pages={32–51}, language={en} }

@book{kortmann2008varieties,
  title={Varieties of English},
  author={Kortmann, Bernd},
  volume={1}
  
}

@misc{alma99153242810001452,
isbn = {9783110196351},
keywords = {English language -- Dialects ; English language -- Variation},
language = {eng},
lccn = {2007045291},
publisher = {Mouton de Gruyter},
series = {Mouton textbook},
title = {Varieties of English.},
url = {https://www.loc.gov/catdir/toc/ecip084/2007045291.html},
year = {2008},
abstract = {"This volume gives a detailed overview of the varieties of English spoken in Africa, South and Southeast Asia, including L1 varieties (such as White South African or St Helena English), L2 varieties (such as Cameroon, Pakistani, or Malaysian English) as well as pidgins and creoles (such as Nigerian or Ghanaian Pidgin). The chapters provide concise and comprehensive information on the phonological, morphological and syntactic characteristics of each variety discussed. The articles are followed by exercises and study questions."--Jacket},
author = {Kortmann, Bernd and Upton, Clive and Schneider, Edgar W. and Burridge, Kate. and Mesthrie, Rajend.},
address = {Berlin ;},
}



@book{trudgill1984language,
  title={Language in the British Isles},
  author={Trudgill, P.},
  isbn={9780521284097},
  lccn={83007616},
  url={https://books.google.com/books?id=liY4AAAAIAAJ},
  year={1984},
  publisher={Cambridge University Press}
}

@article{otieno2024framework,
  title={Framework for building linguistic corpora for a large language model project for the heritage nubian language of kenya},
  author={Otieno, Peter Nyansera},
  journal={Journal of Languages, Linguistics and Literary Studies},
  volume={4},
  number={3},
  pages={139--144},
  year={2024}
}

@phdthesis{wellens2003arabic,
  title={An Arabic creole in Africa: the Nubi language of Uganda},
  author={Wellens, Inneke Hilda Werner},
  year={2003},
  school={[Sl: sn]}
}

@article{owens2006creole,
  title={Creole Arabic},
  author={Owens, Jonathan},
  journal={Encyclopedia of Arabic Language and Linguistics, Leiden--Boston, Brill},
  pages={518--527},
  year={2006}
}

@article{hura1992role,
  title={On the role of perception in shaping phonological assimilation rules},
  author={Hura, Susan L and Lindblom, Bj{\"o}rn and Diehl, Randy L},
  journal={Language and speech},
  volume={35},
  number={1-2},
  pages={59--72},
  year={1992},
  publisher={SAGE Publications Sage UK: London, England}
}

@article{nubi_genetic_owens,
 ISSN = {00035483, 19446527},
 URL = {http://www.jstor.org/stable/30028012},
 abstract = {Creole languages pose a special problem to genetic linguistics. One viewpoint, represented inter alia by Bickerton and by Thomason and Kaufman (albeit in different ways), emphasizes the uniqueness of creoles. A second stresses the historical links of creoles either to substrate languages (Holm 1988) or the lexifier source (Hall 1958). In this paper I compare East African Nubi, an Arabic-based creole, to Sudanic Arabic, and to two potential substrate languages, Bari and Mamvu, and conclude that Sudanic Arabic is the most significant source. It is further suggested that one can speak of Nubi being genetically related to Arabic, and, by implication, that genetic relationship is generally one aspect of a creole's characterization.},
 author = {Jonathan Owens},
 journal = {Anthropological Linguistics},
 number = {1},
 pages = {1--30},
 publisher = {[Anthropological Linguistics, Trustees of Indiana University]},
 title = {Nubi, Genetic Linguistics, and Language Classification},
 urldate = {2026-03-18},
 volume = {33},
 year = {1991}
}

@article{nubi_stress_tone, 
title={Between stress and tone in Nubi word prosody}, 
volume={23}, 
DOI={10.1017/S0952675706000881}, 
number={2}, 
journal={Phonology}, author={Gussenhoven, Carlos}, 
year={2006}, 
pages={192–223}
} 

@article{avram2020substrate,
  title={Substrate and adstrate influence on (Ki) Nubi: Evidence from early records},
  author={Avram, Andrei A},
  journal={Academic Journal of Modern Philology},
  number={10},
  pages={7--21},
  year={2020},
  publisher={Komisja Nauk Filologicznych Oddzia{\l}u Polskiej Akademii Nauk we Wroc{\l}awiu}
}

@book{wellens2005nubi,
  title={The Nubi language of Uganda: an Arabic creole in Africa},
  author={Wellens, Inneke Hilda Werner},
  volume={45},
  year={2005},
  publisher={Brill}
}



@article{morphologization_jonathan,
   author = "Owens, Jonathan",
   title = "The morphologization of an Arabic creole", 
   journal= "Journal of Pidgin and Creole Languages",
   year = "2014",
   volume = "29",
   number = "2",
   pages = "232-298",
   doi = "https://doi.org/10.1075/jpcl.29.2.03owe",
   url = "https://www.jbe-platform.com/content/journals/10.1075/jpcl.29.2.03owe",
   publisher = "John Benjamins",
   issn = "0920-9034",
   type = "Journal Article",
   keywords = "creoles",
   keywords = "theories of language change",
   keywords = "morphological change",
   keywords = "morphological restructuring",
   keywords = "Arabic",
   abstract = "East African Nubi has classic attributes of a creole — it was formed in a short period of time and its structure diverges dramatically from its lexical source, Egyptian and Sudanic Arabic — yet it differs from most creoles as well in that it has a fairly robust morphology (Owens 2001). One could call it a morphologically rich creole, even if its morphology is much simpler than that of Arabic. Understanding why this happened in Nubi presupposes having a solid descriptive historical linguistic account of how this came about. While concentrating on this latter issue, this paper lays the groundwork for understanding the ‘why’ by examining Nubi relative to current theories of creole genesis, including recent SLA models, and by showing that discourse embeddedness played an important role in guiding its development.",
  }

@article { plural_formation_nubi,
      author = "Alain Kihm",
      title = "Plural formation in Nubi and Arabic: A comparative study and a word-based approach",
      journal = "Brill's Journal of Afroasiatic Languages and Linguistics",
      year = "2011",
      publisher = "Brill",
      address = "Leiden, The Netherlands",
      volume = "3",
      number = "1",
      doi = "10.1163/187666311X562431",
      pages=      "1 - 21",
      url = "https://brill.com/view/journals/aall/3/1/article-p1_1.xml"
}

@phdthesis{wellens2003arabic,
  title={An Arabic creole in Africa: the Nubi language of Uganda},
  author={Wellens, Inneke Hilda Werner},
  year={2003},
  school={[Sl: sn]}
}

@article{origin_african_nubi,
 ISSN = {00035483, 19446527},
 URL = {http://www.jstor.org/stable/30032239},
 abstract = {I examine the origins of East African Nubi, an Arabic creole of Uganda and Kenya, concentrating on its Arabic origin, which constitutes its greatest lexical, grammatical and phonological source. Nubi exhibits the greatest degree of affinity with the Arabic dialects of the western Sudan (Nigeria, Chad, western Sudan). There are only two strikingly innovative aspects of Nubi: loss of gemination with its consequences on syllable structure, and a great reduction in inflectional structure. Finally, the status of Nubi as a creole language is considered.},
 author = {Jonathan Owens},
 journal = {Anthropological Linguistics},
 number = {3},
 pages = {229--271},
 publisher = {[Anthropological Linguistics, Trustees of Indiana University]},
 title = {The Origins of East African Nubi},
 urldate = {2026-03-18},
 volume = {27},
 year = {1985}
}

@misc{569683,
  author       = {Campbell, Lyle and Lee, Nala Huiying and Okura, Eve and Simpson, Sean and Ueki, Kaori},
  howpublished = {Database available at http://endangeredlanguages.com/userquery/download/, accessed 2022-08-28.},
  title        = {The Catalogue of Endangered Languages (ElCat)},
  year         = {2022},
  hhtype       = {overview;socling},
  inlg         = {English [eng]},
  macro_area   = {Eurasia},
  src          = {hh}
}

@book{550905,
  address        = {Paris},
  author         = {Moseley, Christopher},
  booktitle      = {Atlas of the world’s languages in danger},
  edition        = {3},
  pages          = {373},
  publisher      = {UNESCO Publishing},
  title          = {Atlas of the world's languages in danger},
  url            = {http://www.unesco.org/culture/en/endangeredlanguages/atlas},
  year           = {2010},
  bestfn         = {eurasia\moseley_atlas-danger2010_o.pdf},
  besttxt        = {ptxt2\eurasia\moseley_atlas-danger2010_o.txt},
  citekeys       = {glossa8730:B42},
  fn             = {eurasia\moseley_atlas-danger2010.pdf, eurasia\moseley_atlas-danger2010_o.pdf},
  hhtype         = {overview;socling},
  inlg           = {English [eng]},
  isbn           = {9789231040955 9231040952},
  isreferencedby = {glossa8730},
  location       = {Paris},
  macro_area     = {Eurasia},
  src            = {glossa, hh, langsci}
}

@misc{glottolog,
  author       = {Hammarström, Harald and Forkel, Robert and Haspelmath, Martin and Bank, Sebastian},
  publisher    = {Leipzig: Max Planck Institute for Evolutionary Anthropology.},
  url          = {https://doi.org/10.5281/zenodo.18840935},
  howpublished = {Available online at http://glottolog.org, Accessed on 2026-03-18.},
  title        = {Glottolog 5.3},
  year         = {2026},

}

@article{amer2023relexification,
  title={Relexification and dialect levelling in the genesis of creoles: the case of the Arabic-based creole, Nubi},
  author={Amer, Ahmed and Iryna, Lenchuk},
  journal={Research Result. Theoretical and Applied Linguistics},
  volume={9},
  number={2},
  pages={49--72},
  year={2023},
  publisher={Belgorod State National Research University},
}

@book{modern_scots,
 ISBN = {9781474416863},
 URL = {http://www.jstor.org/stable/10.3366/j.ctv1kz4g3q},
 abstract = {This textbook overview of Modern Scots provides a description and analysis of the language covering lexical, phonological and structural patterns. It presents evidence for the diversity of the language through illustrations from newly collected fieldwork material.},
 author = {Robert McColl Millar},
 publisher = {Edinburgh University Press},
 title = {Modern Scots: An Analytical Survey},
 urldate = {2026-03-18},
 year = {2018}
}

@book{history_scots,
    author = {Millar, Robert McColl},
    title = {A History of the Scots Language},
    publisher = {Oxford University Press},
    year = {2023},
    month = {09},
    abstract = {This book provides both a sociolinguistic and a linguistic history of the Scots language, spoken both in Scotland and Ulster. It provides this material in two ways, reflecting both the historical, political, and economic forces which affected the status and use of Scots and the linguistic developments through which, like all languages, Scots has passed. Chapters 1 and 2 act as introductions, the first attempting to provide an analysis of what Scots is, in particular in relation to Standard English. The second chapter discusses the Indo-European background of Scots, while also discussing the linguistic ecology of what is now Scotland before Anglian dialects were introduced in the post-Roman period. Chapters 3 and 4 describe the sociolinguistic factors which have influenced the development of the language. Chapter 3 discusses the ‘rise’ of the language to being the de facto official language of Scotland; Chapter 4 considers its ‘fall’, analysing the social, economic, and political forces which led to its dialectalization under English. The formation of Scottish Standard English is also discussed in depth. Chapters 5, 6, and 7 analyse system-internal change in, respectively, the phonological, morphosyntactic, and lexical systems of Scotland. While a coherent narrative is maintained throughout, dialect-based development and change is given prominence. Chapter 8 provides a brief summary of what has been covered in the book, while at the same time suggesting future research projects which might prove productive.},
    isbn = {9780198863991},
    doi = {10.1093/oso/9780198863991.001.0001},
    url = {https://doi.org/10.1093/oso/9780198863991.001.0001},
}

@misc{grammar_scots,
isbn = {0854110682},
keywords = {Scots language -- Grammar ; Scots language -- Usage},
language = {sco;eng},
publisher = {Saltire Society},
title = {A Scots grammar : Scots grammar and usage : Scots that haes-- },
year = {1997},
author = {Purves, David. and Saltire Society.},
address = {Edinburgh},
booktitle = {A Scots grammar : Scots grammar and usage : Scots that haes--},
}

@article{asr_under_rsrc_survey,
title = {Automatic speech recognition for under-resourced languages: A survey},
journal = {Speech Communication},
volume = {56},
pages = {85-100},
year = {2014},
issn = {0167-6393},
doi = {https://doi.org/10.1016/j.specom.2013.07.008},
url = {https://www.sciencedirect.com/science/article/pii/S0167639313000988},
author = {Laurent Besacier and Etienne Barnard and Alexey Karpov and Tanja Schultz},
keywords = {Under-resourced languages, Automatic speech recognition (ASR), Language portability, Speech and language resources acquisition, Statistical language modeling, Crosslingual acoustic modeling and adaptation, Automatic pronunciation generation, Lexical modeling},
abstract = {Speech processing for under-resourced languages is an active field of research, which has experienced significant progress during the past decade. We propose, in this paper, a survey that focuses on automatic speech recognition (ASR) for these languages. The definition of under-resourced languages and the challenges associated to them are first defined. The main part of the paper is a literature review of the recent (last 8years) contributions made in ASR for under-resourced languages. Examples of past projects and future trends when dealing with under-resourced languages are also presented. We believe that this paper will be a good starting point for anyone interested to initiate research in (or operational development of) ASR for one or several under-resourced languages. It should be clear, however, that many of the issues and approaches presented here, apply to speech technology in general (text-to-speech synthesis for instance).}
}

@article{nmt_low_rsrc,
author = {Ranathunga, Surangika and Lee, En-Shiun Annie and Prifti Skenduli, Marjana and Shekhar, Ravi and Alam, Mehreen and Kaur, Rishemjit},
title = {Neural Machine Translation for Low-resource Languages: A Survey},
year = {2023},
issue_date = {November 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {55},
number = {11},
issn = {0360-0300},
url = {https://doi.org/10.1145/3567592},
doi = {10.1145/3567592},
abstract = {Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource language pairs remains sub-optimal compared to the high-resource counterparts due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight recently, thus leading to substantial research on this topic. This article presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT) and quantitative analysis to identify the most popular techniques. We provide guidelines to select the possible NMT technique for a given LRL data setting based on our findings. We also present a holistic view of the LRL-NMT research landscape and provide recommendations to enhance the research efforts further.},
journal = {ACM Comput. Surv.},
month = feb,
articleno = {229},
numpages = {37},
keywords = {pivoting, zero-shot translation, data augmentation, transfer learning, multilingual NMT, semi-supervised NMT, unsupervised NMT, low-resource languages, Neural machine translation}
}

@ARTICLE{asr_vietnamese,
  author={Le, Viet-Bac and Besacier, Laurent},
  journal={IEEE Transactions on Audio, Speech, and Language Processing}, 
  title={Automatic Speech Recognition for Under-Resourced Languages: Application to Vietnamese Language}, 
  year={2009},
  volume={17},
  number={8},
  pages={1471-1482},
  keywords={Natural languages;Automatic speech recognition;Dictionaries;Lattices;Speech synthesis;Context modeling;Speech processing;Proposals;Character generation;Error analysis;Crosslingual acoustic modeling;grapheme-based acoustic modeling;lattice decomposition and combination;speech recognition;under-resourced languages},
  doi={10.1109/TASL.2009.2021723}}

@inproceedings{asr_hci,
author = {Reitmaier, Thomas and Wallington, Electra and Kalarikalayil Raju, Dani and Klejch, Ondrej and Pearson, Jennifer and Jones, Matt and Bell, Peter and Robinson, Simon},
title = {Opportunities and Challenges of Automatic Speech Recognition Systems for Low-Resource Language Speakers},
year = {2022},
isbn = {9781450391573},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3491102.3517639},
doi = {10.1145/3491102.3517639},
abstract = {Automatic Speech Recognition (ASR) researchers are turning their attention towards supporting low-resource languages, such as isiXhosa or Marathi, with only limited training resources. We report and reflect on collaborative research across ASR \& HCI to situate ASR-enabled technologies to suit the needs and functions of two communities of low-resource language speakers, on the outskirts of Cape Town, South Africa and in Mumbai, India. We build on longstanding community partnerships and draw on linguistics, media studies and HCI scholarship to guide our research. We demonstrate diverse design methods to: remotely engage participants; collect speech data to test ASR models; and ultimately field-test models with users. Reflecting on the research, we identify opportunities, challenges, and use-cases of ASR, in particular to support pervasive use of WhatsApp voice messaging. Finally, we uncover implications for collaborations across ASR \& HCI that advance important discussions at CHI surrounding data, ethics, and AI.},
booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems},
articleno = {299},
numpages = {17},
keywords = {Speech/language, automatic speech recognition, mobile devices},
location = {New Orleans, LA, USA},
series = {CHI '22}
}

@article{mixtec_ipa, title={San Martín Peras Mixtec}, volume={54}, DOI={10.1017/S0025100324000124}, number={2}, journal={Journal of the International Phonetic Association}, author={Eischens, Ben and Hedding, Andrew A.}, year={2024}, pages={811–852}} <div></div>

@online{ethnologue,
  author       = {{SIL International}},
  title        = {Ethnologue: Languages of the World},
  year         = {2024},
  howpublished = {\url{https://www.ethnologue.com}},
  note         = {Accessed: 2026-03-23}
}

@online{mozilla_dataset2026,
  author    = {{Mozilla Data Collective}},
  title     = {Dataset: Mozilla Data Collective},
  year      = {2026},
  url       = {https://datacollective.mozillafoundation.org/datasets/cmfzu8u8wa555eq8onrk334h4},
  note      = {Accessed: 2026-03-23}
}

@article{ml_asr_review,
  title={Machine Learning Paradigms for Speech Recognition: An Overview},
  author={Li Deng and Xiao Li},
  journal={IEEE Transactions on Audio, Speech, and Language Processing},
  year={2013},
  volume={21},
  pages={1060-1089},
  url={https://api.semanticscholar.org/CorpusID:16585863}
}

@misc{meta_omnilingual_asr_blog,
      title={Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages}, 
      author={Omnilingual and Gil Keren and Artyom Kozhevnikov and Yen Meng and Christophe Ropers and Matthew Setzler and Skyler Wang and Ife Adebara and Michael Auli and Can Balioglu and Kevin Chan and Chierh Cheng and Joe Chuang and Caley Droof and Mark Duppenthaler and Paul-Ambroise Duquenne and Alexander Erben and Cynthia Gao and Gabriel Mejia Gonzalez and Kehan Lyu and Sagar Miglani and Vineel Pratap and Kaushik Ram Sadagopan and Safiyyah Saleem and Arina Turkatenko and Albert Ventayol-Boada and Zheng-Xin Yong and Yu-An Chung and Jean Maillard and Rashel Moritz and Alexandre Mourachko and Mary Williamson and Shireen Yates},
      year={2025},
      eprint={2511.09690},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2511.09690}, 
}

@article{asr_end_lng_doc,
title = {Improving ASR Output for Endangered Language Documentation}, url = {https://par.nsf.gov/biblio/10087204}, 
DOI = {10.21437/SLTU.2018-39}, 
abstractNote = {Documenting endangered languages supports the historical preservation of diverse cultures. Automatic speech recognition (ASR), while potentially very useful for this task, has been underutilized for language documentation due to the challenges inherent in building robust models from extremely limited audio and text training resources. In this paper, we explore the utility of supplementing existing training resources using synthetic data, with a focus on Seneca, a morphologically complex endangered language of North America. We use transfer learning to train acoustic models using both the small amount of available acoustic training data and artificially distorted copies of that data. We then supplement the language model training data with verb forms generated by rule and sentences produced by an LSTM trained on the available text data. The addition of synthetic data yields reductions in word error rate, demonstrating the promise of data augmentation for this task.}, 
journal = {The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages}, 
author = {Jimerson, Robert and Simha, Kruthika and Ptucha, Ray and Prud'hommeaux, Emily}, 
}

@article{prud2021automatic,
  title={Automatic speech recognition for supporting endangered language documentation},
  author={Prud’hommeaux, Emily and Jimerson, Robbie and Hatcher, Richard and Michelson, Karin},
  year={2021},
  journal = {Language Documentation & Conservation},
    volume={15},
  pages={491-513},
  publisher={University of Hawaii Press},
  url={http://hdl.handle.net/10125/74666}, 
}

@ARTICLE{machine_chain,
  author={Tjandra, Andros and Sakti, Sakriani and Nakamura, Satoshi},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, 
  title={Machine Speech Chain}, 
  year={2020},
  volume={28},
  number={},
  pages={976-989},
  keywords={Speech processing;Data models;Machine learning;Task analysis;Training;Hidden Markov models;Speech chain;ASR;TTS;deep learning},
  doi={10.1109/TASLP.2020.2977776}}

@article{asr_lg_learn,
author = {Joost van Doremalen and Lou Boves and Jozef Colpaert and Catia Cucchiarini and Helmer Strik},
title = {Evaluating automatic speech recognition-based language learning systems: a case study},
journal = {Computer Assisted Language Learning},
volume = {29},
number = {4},
pages = {833--851},
year = {2016},
publisher = {Routledge},
doi = {10.1080/09588221.2016.1167090},
URL = {
https://doi.org/10.1080/09588221.2016.1167090
},
eprint = {
https://doi.org/10.1080/09588221.2016.1167090
}
}

@article{sec_lg_learning,
  title={Automatic Speech Recognition for second language learning: How and why it actually works.},
  year={2003},
  publisher={Barcelona: sn}
}

@article{sun_lg_learn,
    
AUTHOR={Sun, Weina },
           
TITLE={The impact of automatic speech recognition technology on second language pronunciation and speaking skills of EFL learners: a mixed methods investigation},
          
JOURNAL={Frontiers in Psychology},
          
VOLUME={Volume 14 - 2023},
  
YEAR={2023},
  
URL={https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1210187},
  
DOI={10.3389/fpsyg.2023.1210187},
  
ISSN={1664-1078},
  
ABSTRACT={This study used an explanatory sequential design to investigate the effectiveness of automatic speech recognition technology (ASR) with peer correction in enhancing second language (L2) pronunciation and speaking performance of English as a Foreign Language (EFL) learners. A total of 61 intermediate-level Chinese EFL learners were randomly assigned to either a control group (CG) receiving traditional teacher-led feedback and instruction or an experimental group (EG) using ASR technology with peer correction. Read-aloud tasks, spontaneous conversations, and IELTS speaking tests were used to collect data for L2 pronunciation and speaking skill. Also, semistructured interviews were carried out with a number of the participants to explore their perceptions of the ASR technology. The quantitative results showed that the EG outperformed the CG in all measures of L2 pronunciation, including accentedness and comprehensibility. Additionally, the EG demonstrated significant greater improvements in global speaking skill. The qualitative analysis of the interviews revealed that the majority of the participants found the ASR technology helpful in improving their L2 pronunciation and speaking skills. Overall, the findings suggest that ASR technology with peer correction can be an effective tool in enhancing L2 pronunciation and speaking skills in EFL learners.}}

@article{lang_learn_journal,
author = {Xiao, Wenqi and Park, Moonyoung},
title = {Using Automatic Speech Recognition to Facilitate English Pronunciation Assessment and Learning in an EFL Context: Pronunciation Error Diagnosis and Pedagogical Implications},
year = {2021},
issue_date = {Jul 2021},
publisher = {IGI Global},
address = {USA},
volume = {11},
number = {3},
issn = {2155-7098},
url = {https://doi.org/10.4018/IJCALLT.2021070105},
doi = {10.4018/IJCALLT.2021070105},
abstract = {With the advancement of automatic speech recognition (ASR) technology, ASR-based pronunciation assessment can diagnose learners' pronunciation problems. Meanwhile, ASR-based pronunciation training allows more opportunities for pronunciation practice. This study aims to investigate the effectiveness of ASR technology in diagnosing English pronunciation errors and to explore teachers' and learners' attitudes towards using ASR technology as a pronunciation assessment tool and as a learning tool. Five Chinese EFL learners participated in read-aloud tests, including a human-assessed test and an ASR-assessed test. Pronunciation error types diagnosed by the two tests were compared to determine the extent of overlapping areas. The findings demonstrate that there were overlaps between human rating and machine rating at the segmental level. Moreover, it was found that learners' varied pronunciation learning needs were met by using the ASR technology. Implications of the study will provide insights relevant to using ASR technology to facilitate English pronunciation assessment and learning.},
journal = {Int. J. Comput.-Assist. Lang. Learn. Teach.},
month = jul,
pages = {74–91},
numpages = {18},
keywords = {Automatic Speech Recognition, English Pronunciation Assessment, English Pronunciation Learning}
}

@article{asr_accessibility_1,
  title={Universal access to communication and learning: the role of automatic speech recognition},
  author={Wald, Mike and Bain, Keith},
  journal={Universal Access in the Information Society},
  volume={6},
  number={4},
  pages={435--447},
  year={2008},
  publisher={Springer},
  doi = {https://doi.org/10.1007/s10209-007-0093-9}
}

@inproceedings{asr_accessibility_2,
author = {Butler, Janine and Trager, Brian and Behm, Byron},
title = {Exploration of Automatic Speech Recognition for Deaf and Hard of Hearing Students in Higher Education Classes},
year = {2019},
isbn = {9781450366762},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3308561.3353772},
doi = {10.1145/3308561.3353772},
abstract = {Automatic speech recognition (ASR) programs that generate real-time speech-to-text captions can be provided as supplemental access technologies for deaf and hard of hearing (DHH) students in higher education classes. As part of a pilot program, we implemented ASR as a supplemental access service in biology, statistics, and other courses at our university. To identify the benefits and limitations of ASR as an access technology, we surveyed 26 DHH students and interviewed 8 of these students about their experiences with ASR in their mainstream classes. Participants believed that ASR was beneficial despite the errors that ASR continued to generate; however, the accuracy and readability of ASR need to improve so that students can better access spoken information through ASR. This paper reviews points for researchers to consider when designing and providing ASR as a supplemental access service in educational settings.},
booktitle = {Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility},
pages = {32–42},
numpages = {11},
keywords = {automatic speech recognition, deaf and hard of hearing, feedback, real-time captions},
location = {Pittsburgh, PA, USA},
series = {ASSETS '19}
}

@article{asr_accessibility_3,
author = {Santiago Omar Caballero Morales and Gladys Bonilla Enríquez and Felipe Trujillo Romero},
title ={Speech-Based Human and Service Robot Interaction: An Application for Mexican Dysarthric People},
journal = {International Journal of Advanced Robotic Systems},
volume = {10},
number = {1},
pages = {11},
year = {2013},
doi = {10.5772/54001},
URL = { 
        https://doi.org/10.5772/54001
},
eprint = { 
        https://doi.org/10.5772/54001
}
,
    abstract = { Dysarthria is a motor speech disorder due to weakness or poor coordination of the speech muscles. This condition can be caused by a stroke, traumatic brain injury, or by a degenerative neurological disease. Commonly, people with this disorder also have muscular dystrophy, which restricts their use of switches or keyboards for communication or control of assistive devices (i.e., an electric wheelchair or a service robot). In this case, speech recognition is an attractive alternative for interaction and control of service robots, despite the difficulty of achieving robust recognition performance. In this paper we present a speech recognition system for human and service robot interaction for Mexican Spanish dysarthric speakers. The core of the system consisted of a Speaker Adaptive (SA) recognition system trained with normal-speech. Features such as on-line control of the language model perplexity and the adding of vocabulary, contribute to high recognition performance. Others, such as assessment and text-to-speech (TTS) synthesis, contribute to a more complete interaction with a service robot. Live tests were performed with two mild dysarthric speakers, achieving recognition accuracies of 90–95\% for spontaneous speech and 95–100\% of accomplished simulated service robot tasks. }
}

@article{asr_accessibility_4,
author = {Guo, Anhong and Kamar, Ece and Vaughan, Jennifer Wortman and Wallach, Hanna and Morris, Meredith Ringel},
title = {Toward fairness in AI for people with disabilities SBG@a research roadmap},
year = {2020},
issue_date = {October 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
number = {125},
issn = {1558-2337},
url = {https://doi.org/10.1145/3386296.3386298},
doi = {10.1145/3386296.3386298},
abstract = {AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.},
journal = {SIGACCESS Access. Comput.},
month = mar,
articleno = {2},
numpages = {1}
}

@article{preserve_asr_1,
  title={Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges},
  author={Vincent Koc},
  journal={ArXiv},
  year={2025},
  volume={abs/2501.11496},
  url={https://api.semanticscholar.org/CorpusID:275758207}
}

@Inbook{preserve_asr_2,
author="Gedeon, Mugisho Matabaro
and Samantaray, Swati
and Ren{\'e}, Kwigomba Bulonza",
editor="Mohanty, Sushree Sangita
and Dash, Satya Ranjan
and Parida, Shantipriya",
title="Changing the Trajectory: Preserving the Linguistic Diversity of Shi Language Using AI and NLP",
bookTitle="Applying AI-Based Tools and Technologies Towards Revitalization of Indigenous and Endangered Languages",
year="2024",
publisher="Springer Nature Singapore",
address="Singapore",
pages="57--69",
abstract="This research explores the significance of utilizing AI and NLP in the conservation and revitalization of endangered languages, with a specific focus on the Shi language spoken by approximately 1,725,000 people in the East of the Democratic Republic of Congo. The Shi language, written in the Latin alphabet, faces the risk of extinction due to decreasing usage among younger generations who are shifting to Swahili and French. This chapter highlights the importance of AI in preserving the cultural heritage embedded within Indigenous languages like Shi, emphasizing the need for technological interventions to reverse language decline. Hence, this study emphasizes the significance of AI and NLP in preserving cultural heritage and linguistic diversity, drawing from previous research on the Shi language, available data, and experiences from other Indigenous language revitalization efforts.",
isbn="978-981-97-1987-7",
doi="10.1007/978-981-97-1987-7_5",
url="https://doi.org/10.1007/978-981-97-1987-7_5"
}

@inproceedings{preserve_asr_3,
  title={Using AI to help preserve indigenous oral histories},
  author={Dueck, Gerhard W},
  booktitle={2024 IEEE International Humanitarian Technologies Conference (IHTC)},
  pages={1--5},
  year={2024},
  organization={IEEE}
}

@Inbook{preserve_asr_4,
author="Murshed, Aref A.
and alrahamneh, Ali
and Alhalalmeh, Al-Hareth
and Al-Badawi, Mohammed",
editor="Musleh Al-Sartawi, Abdalmuttaleb M. A.
and Nour, Abdulnaser Ibrahim
and Abdeljawad, Islam",
title="The Role of Technology in Preserving Indigenous Cultures and Languages",
bookTitle="Business Resilience and Business Innovation for Sustainability: The Double-Edged Role of Artificial Intelligence and Other Disruptive Technologies",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="2399--2409",
abstract="This chapter aims to reveal the role of technology in preserving indigenous cultures and languages, which are increasingly at risk due to globalization and advancements in technology. Indigenous languages serve not only as means of communication but are also vital channels for maintaining cultural heritage, national identity and collective memory. The extinction of these languages can lead to the erosion of cultural traditions and, consequently, the societies that uphold them. Therefore, this study employs a qualitative research design utilizing descriptive and analytical methods to explore how technological innovations, including digital platforms, language-learning apps and artificial intelligence, can support the documentation, teaching and revitalization of endangered languages. It systematically reviews existing research and case studies to provide a comprehensive overview of technology's impact on the preservation of indigenous languages. The study critically evaluates the achievements and obstacles faced by current technological initiatives and offers actionable recommendations to improve their effectiveness. The findings emphasize the need for strategic policies that position technology as a means to enhance language diversity and empower marginalized communities through cultural resilience.",
isbn="978-3-031-87584-7",
doi="10.1007/978-3-031-87584-7_175",
url="https://doi.org/10.1007/978-3-031-87584-7_175"
}

