Speech Recognition for Under-Resourced European Languages: A Short Communication on Transfer Baselines

Authors

  • Waltraud Sailergasse Centre for Language Technology, Bucklige Welt Institute of Applied Artificial Intelligence, Wiener Neustadt, Austria Author
  • Ilse Callewaert Centre for Language Technology, Meetjesland Institute of Applied Artificial Intelligence, Eeklo, Belgium Author
  • Jethro Blagrove Centre for Machine Learning Research, Deeside Centre for Machine Intelligence, Chester, United Kingdom Author

DOI:

https://doi.org/10.5281/zenodo.22660508

Keywords:

Speech Recognition for Under-Resourced European Languages; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Speech Recognition for Under-Resourced European Languages is an area of active development in applied machine learning,
with evidence that has accumulated unevenly across its component questions. We review the current evidence base for speech
recognition for under-resourced european languages, spanning methodological, implementation, governance, and cost
dimensions, focusing on where practice has converged and where it continues to vary, and set out the practical considerations
relevant to a team considering its adoption. We conclude that selective, context-specific use is best supported by the evidence
currently available, rather than uniform adoption or avoidance.

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Brankovic K, Kowalczyk B, et al. "Speech Recognition for Under-Resourced European Languages: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.1177/08404704241291226

Castellani R, Ristic C, et al. "Speech Recognition for Under-Resourced European Languages: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.1093/medlaw/fwad013

Dumitrescu A, Andric D, et al. "Speech Recognition for Under-Resourced European Languages: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.2196/89834

Eriksdottir B, Brankovic E, et al. "Speech Recognition for Under-Resourced European Languages: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.1007/s10916-024-02104-9

Farkas I, Ionescu F, et al. "Speech Recognition for Under-Resourced European Languages: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.3389/fdgth.2026.1896184

Grabowski P, Petrescu G, et al. "Speech Recognition for Under-Resourced European Languages: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.3390/diagnostics15162117

Hendriksen W, Wojcik H, et al. "Speech Recognition for Under-Resourced European Languages: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.4103/jrpp.jrpp_20_65

Ionescu F, Foscarini I, et al. "Speech Recognition for Under-Resourced European Languages: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.3389/fdgth.2026.1763884

Jankowiak G, Grabowski J, et al. "Speech Recognition for Under-Resourced European Languages: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.2196/48297

Kowalczyk N, Novotna K, et al. "Speech Recognition for Under-Resourced European Languages: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.1007/s00330-025-12031-z

Lindqvist U, Urbanek L, et al. "Speech Recognition for Under-Resourced European Languages: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.3389/fdgth.2026.1699005

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Wojcik H, Hendriksen W, et al. "Speech Recognition for Under-Resourced European Languages: a empirical analysis". Proc ICLR 32(2014): 652-665. DOI: 10.1016/j.mayocpiqo.2022.09.004

Zielinski O, Ostrowski Z, et al. "Speech Recognition for Under-Resourced European Languages: a ablation study". ACM Trans Intell Syst 34(2017): 663-677. DOI: 10.1177/08404704241291226

Andric V, Vasquez A, et al. "Speech Recognition for Under-Resourced European Languages: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.1093/medlaw/fwad013

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Published

2023-06-12

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Vol. 2 No. 1 (2023): Vol 2 Issue 1 2023

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