Working bibliography in APA style, grouped by category. The list is indicative and subject to verification before final submission.
Note
The references below constitute a working bibliography in APA style and may include items whose volume, page numbers, or DOI are pending final verification. The list is updated incrementally as the manuscript is finalized. Each entry is linked to its source where a DOI, arXiv identifier, or URL is available; otherwise a “Find / consult” link opens a Google Scholar (or Google Books) search so the work can be located, consulted, or purchased.
A
Books and monographs
Bahl, L. R., Jelinek, F., & Mercer, R. L. (1983). A maximum likelihood approach to continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(2), 179–190. Find this book ↗
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Find this book ↗
Blust, R. (2013). The Austronesian languages (Rev. ed.). Asia‑Pacific Linguistics, Australian National University. Find this book ↗
Bouckaert, R., Bowern, C., & Atkinson, Q. D. (2018). The origin and expansion of Pama‑Nyungan languages across Australia. Nature Ecology & Evolution, 2, 741–749. [Cited as methodological template for Austronesian Bayesian phylogenetic analysis.] Find this book ↗
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Find this book ↗
Huang, X., Acero, A., & Hon, H.‑W. (2001). Spoken language processing. Prentice Hall. Find this book ↗
Jelinek, F. (1997). Statistical methods for speech recognition. MIT Press. Find this book ↗
Jurafsky, D., & Martin, J. H. (2024). Speech and language processing (3rd ed., draft). Stanford University. Find this book ↗
Keenan, E. L. (1976). Remarkable subjects in Malagasy. In C. N. Li (Ed.), Subject and topic (pp. 247–301). Academic Press. Find this book ↗
Murphy, K. P. (2023). Probabilistic machine learning: Advanced topics. MIT Press. Find this book ↗
Rabiner, L. R., & Juang, B.‑H. (1993). Fundamentals of speech recognition. Prentice Hall. Find this book ↗
Rajemisa‑Raolison, R. (1971). Grammaire malgache. Imprimerie Saint‑Paul. Find this book ↗
Rasoloson, J., & Rubino, C. (2005). Malagasy. In K. A. Adelaar & N. Himmelmann (Eds.), The Austronesian languages of Asia and Madagascar (pp. 456–488). Routledge. Find this book ↗
B
Journal articles
Beal, M. J. (2003). Variational algorithms for approximate Bayesian inference (Doctoral dissertation). University College London. Find / consult ↗
Ganchev, K., Graça, J., Gillenwater, J., & Taskar, B. (2010). Posterior regularization for structured latent variable models. Journal of Machine Learning Research, 11, 2001–2049. Find / consult ↗
Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. Proceedings of ICML, 369–376. Find / consult ↗
Gray, R. D., Drummond, A. J., & Greenhill, S. J. (2009). Language phylogenies reveal expansion pulses and pauses in Pacific settlement. Science, 323(5913), 479–483. Find / consult ↗
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29(6), 82–97. Find / consult ↗
Mann, G. S., & McCallum, A. (2010). Generalized expectation criteria for semi‑supervised learning with weakly labeled data. Journal of Machine Learning Research, 11, 955–984. Find / consult ↗
Naeini, M. P., Cooper, G. F., & Hauskrecht, M. (2015). Obtaining well calibrated probabilities using Bayesian binning. Proceedings of AAAI, 2901–2907. Find / consult ↗
Neal, R. M., & Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. Jordan (Ed.), Learning in graphical models (pp. 355–368). Kluwer. Find / consult ↗
Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3), 61–74. Find / consult ↗
Ponti, E. M., O'Horan, H., Berzak, Y., Vulić, I., Reichart, R., Poibeau, T., Shutova, E., & Korhonen, A. (2019). Modelling language variation and universals: A survey on typological linguistics for natural language processing. Computational Linguistics, 45(3), 559–601. Find / consult ↗
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286. Find / consult ↗
Schultz, T., & Waibel, A. (2001). Language‑independent and language‑adaptive acoustic modeling for speech recognition. Speech Communication, 35(1–2), 31–51. Find / consult ↗
Yu, D., Li, J., & Deng, L. (2011). Calibration of confidence measures in speech recognition. IEEE Transactions on Audio, Speech, and Language Processing, 19(8), 2461–2473. Find / consult ↗
C
Conference papers (ASR, NLP, ML)
Adams, O., Wiesner, M., Watanabe, S., & Yarowsky, D. (2019). Massively multilingual adversarial speech recognition. Proceedings of NAACL‑HLT, 96–108. Find / consult ↗
Adilazuarda, M. F., Cahyawijaya, S., Aji, A. F., Winata, G. I., & Purwarianti, A. (2024). LinguAlchemy: Fusing typological and geographical elements for unseen language generalization. arXiv preprintarXiv:2401.06034.
Aggarwal, V., Nair, S. S., Verma, Y., & Jogi, Y. (2025). Adopting Whisper for confidence estimation. Proceedings of ICASSP. arXiv:2502.13446.
Aisyah, R., Cahyawijaya, S., Adilazuarda, M. F., & Purwarianti, A. (2024). Enhancing Indonesian automatic speech recognition: Evaluating multilingual models with diverse speech variabilities. arXiv preprintarXiv:2410.08828.
Alabi, J. O., et al. (2025). AfriHuBERT: A self‑supervised speech representation model for African languages. Proceedings of Interspeech. arXiv:2409.20201.
Aubin, B., et al. (2025). When models don't collapse: On the consistency of iterative MLE. arXiv preprintarXiv:2505.19046.
Babu, A., Wang, C., Tjandra, A., Lakhotia, K., Xu, Q., Goyal, N., et al. (2022). XLS‑R: Self‑supervised cross‑lingual speech representation learning at scale. Proceedings of Interspeech. Find / consult ↗
Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. (2020). wav2vec 2.0: A framework for self‑supervised learning of speech representations. Advances in Neural Information Processing Systems, 33. Find / consult ↗
Bellare, K., Druck, G., & McCallum, A. (2009). Alternating projections for learning with expectation constraints. Proceedings of UAI. arXiv:1205.2660.
Bertrand, Q., Bose, A. J., Duplessis, A., Jiralerspong, M., & Gidel, G. (2024). On the stability of iterative retraining of generative models. Proceedings of ICLR. Find / consult ↗
Bhogale, K., et al. (2024). Empowering low‑resource language ASR via large‑scale pseudo labelling. Proceedings of Interspeech. Find / consult ↗
Baevski, A., Hsu, W.‑N., Xu, Q., Babu, A., Gu, J., & Auli, M. (2022). data2vec: A general framework for self‑supervised learning in speech, vision and language. Proceedings of ICML. Find / consult ↗
Battenberg, E., Chen, J., Child, R., Coates, A., Li, Y., Liu, H., et al. (2017). Exploring neural transducers for end‑to‑end speech recognition. Proceedings of ASRU. Find / consult ↗
Carofilis, A., Burdisso, S., Madikeri, S., et al. (2025). Better semi‑supervised learning for multi‑domain ASR through incremental retraining and data filtering. Proceedings of Interspeech. arXiv:2506.04981.
Chan, T. T., Tong, X., Hoang, T. T. U., Tepnadze, B., & Stempniak, W. (2025). Towards typologically aware rescoring to mitigate unfaithfulness in lower‑resource languages. ISCA/ITG Workshop on Diversity in Large Speech and Language Models. arXiv:2502.17664.
Chan, W., Jaitly, N., Le, Q., & Vinyals, O. (2016). Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. Proceedings of ICASSP. Find / consult ↗
Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., et al. (2022). WavLM: Large‑scale self‑supervised pre‑training for full stack speech processing. IEEE Journal of Selected Topics in Signal Processing, 16(6), 1505–1518. Find / consult ↗
Chowdhury, S. A., Hussein, A., Abdelali, A., & Ali, A. (2021). Towards one model to rule all: Multilingual strategy for dialectal code‑switching Arabic ASR. Proceedings of Interspeech, 2466–2470. Find / consult ↗
Communication, S. (Seamless Communication Team). (2024). Seamless: Multilingual expressive and streaming speech translation. arXiv preprintarXiv:2312.05187.
Conneau, A., Baevski, A., Collobert, R., Mohamed, A., & Auli, M. (2020). Unsupervised cross‑lingual representation learning for speech recognition. Proceedings of Interspeech. Find / consult ↗
Conneau, A., Ma, M., Khanuja, S., Zhang, Y., Axelrod, V., Dalmia, S., et al. (2022). FLEURS: Few‑shot learning evaluation of universal representations of speech. Proceedings of SLT. Find / consult ↗
Coto‑Solano, R., et al. (2024). Synthesized data selection via score distribution matching for Te Reo Māori ASR. TechRxiv preprint. Find / consult ↗
Cui, Y., Cahyawijaya, S., et al. (2025). FormosanBench: Benchmarking low‑resource Austronesian languages in the era of LLMs. Findings of EMNLP. arXiv:2506.21563.
Currey, A., et al. (2024). Multilingual self‑supervised speech representations improve the speech recognition of low‑resource African languages with code‑switching. arXiv preprintarXiv:2311.15077.
Das, D., & Petrov, S. (2013). Cross‑lingual discriminative learning of sequence models with posterior regularization. Proceedings of EMNLP. Find / consult ↗
Diarra, Y., et al. (2025). Dealing with the hard facts of low‑resource African NLP (Bambara). arXiv preprintarXiv:2511.18557.
Fiscus, J. G. (1997). A post‑processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER). Proceedings of IEEE ASRU, 347–354. Find / consult ↗
Gitman, I., Lavrukhin, V., Laptev, A., & Ginsburg, B. (2023). Confidence‑based ensembles of end‑to‑end speech recognition models. Proceedings of Interspeech, 1414–1418. Find / consult ↗
Graves, A. (2012). Sequence transduction with recurrent neural networks. arXiv preprintarXiv:1211.3711.
Gulati, A., Qin, J., Chiu, C.‑C., Parmar, N., Zhang, Y., Yu, J., et al. (2020). Conformer: Convolution‑augmented Transformer for speech recognition. Proceedings of Interspeech. Find / consult ↗
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. Proceedings of ICML. Find / consult ↗
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., et al. (2019). Parameter‑efficient transfer learning for NLP. Proceedings of ICML. Find / consult ↗
Hsu, W.‑N., Bolte, B., Tsai, Y.‑H. H., Lakhotia, K., Salakhutdinov, R., & Mohamed, A. (2021). HuBERT: Self‑supervised speech representation learning by masked prediction of hidden units. IEEE/ACM Transactions on Audio, Speech and Language Processing, 29, 3451–3460. Find / consult ↗
Hsu, W.‑N., Likhomanenko, T., Xu, Q., Synnaeve, G., & Collobert, R. (2022). Pseudo‑labeling for massively multilingual speech recognition. Proceedings of ICASSP. arXiv:2111.00161.
Hsu, W.‑N., Zhang, Y., & Glass, J. (2017). Unsupervised learning of disentangled and interpretable representations from sequential data (FHVAE). Advances in Neural Information Processing Systems, 30. Find / consult ↗
Hu, E. J., Shen, Y., Wallis, P., Allen‑Zhu, Z., Li, Y., Wang, S., et al. (2022). LoRA: Low‑rank adaptation of large language models. Proceedings of ICLR. Find / consult ↗
Hu, Y., Chen, C., Yang, C.‑H. H., Qin, C., Chen, P.‑Y., Chng, E. S., & Zhang, C. (2024). Self‑Taught Recognizer (STAR): Toward unsupervised adaptation for speech foundation models. Advances in Neural Information Processing Systems. arXiv:2405.14161.
Huo, M., Zhang, Y., & Tang, Y. (2025). Identifying and calibrating overconfidence in noisy speech recognition. Proceedings of ASRU. arXiv:2509.07195.
Kannan, A., Wu, Y., Nguyen, P., Sainath, T. N., Chen, Z., & Prabhavalkar, R. (2018). An analysis of incorporating an external language model into a sequence‑to‑sequence model. Proceedings of ICASSP. Find / consult ↗
Khan, A. F., et al. (2024). URIEL+: Enhancing linguistic inclusion and usability in a typological and multilingual knowledge base. arXiv preprintarXiv:2409.18472.
Khare, S., Sharma, N., & Murthy, H. (2024). Initial decoding with minimally augmented language model for improved lattice rescoring in low‑resource ASR. arXiv preprintarXiv:2403.10937.
Khosravani, A., et al. (2021). Modeling dialectal variation for Swiss German automatic speech recognition. Proceedings of Interspeech. Find / consult ↗
Khurana, S., Moritz, N., Hori, T., & Le Roux, J. (2022). Momentum pseudo‑labeling for semi‑supervised speech recognition. arXiv preprintarXiv:2106.08922.
Kim, S., Hori, T., & Watanabe, S. (2017). Joint CTC‑attention based end‑to‑end speech recognition. Proceedings of ICASSP. Find / consult ↗
Klakow, D. (1998). Log‑linear interpolation of language models. Proceedings of ICSLP. Find / consult ↗
Kulkarni, A., Kulkarni, A., Couceiro, M., et al. (2023). Adapting the adapters for code‑switching in multilingual ASR. arXiv preprintarXiv:2310.07423.
Kulkarni, A., et al. (2024). Empowering low‑resource language ASR via large‑scale pseudo labeling (Pratinidhi). arXiv preprintarXiv:2408.14026.
Kumar, A., Singh, S., Gowda, D., Garg, A., Singh, S., & Kim, C. (2021). Multi‑task learning for end‑to‑end ASR word and utterance confidence with deletion prediction. Proceedings of Interspeech. Find / consult ↗
Laptev, A., Andrusenko, A., Podluzhny, I., Mitrofanov, A., Medennikov, I., & Khokhlov, Y. (2023). Fast entropy‑based methods of word‑level confidence estimation for end‑to‑end automatic speech recognition. Proceedings of SLT. Find / consult ↗
Li, B., Sainath, T. N., Sim, K. C., Bacchiani, M., Weinstein, E., Nguyen, P., Chen, Z., Wu, Y., & Rao, K. (2018). Multi‑dialect speech recognition with a single sequence‑to‑sequence model. Proceedings of ICASSP. Find / consult ↗
Lienen, J., & Hüllermeier, E. (2021). Credal self‑supervised learning. Advances in Neural Information Processing Systems, 34. Find / consult ↗
Likhomanenko, T., Lugosch, L., & Collobert, R. (2023). Continuous soft pseudo‑labeling in ASR. I (Still) Can't Believe It's Not Better Workshop, PMLR 187. arXiv:2211.06007.
Littell, P., Mortensen, D. R., Lin, K., Kairis, K., Turner, C., & Levin, L. (2017). URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors. Proceedings of EACL, 8–14. Find / consult ↗
Mann, G. S., & McCallum, A. (2008). Generalized expectation criteria for semi‑supervised learning of conditional random fields. Proceedings of ACL‑08: HLT, 870–878. Find / consult ↗
Manohar, V., Likhomanenko, T., Xu, Q., Hsu, W.‑N., Collobert, R., Saraf, Y., et al. (2021). Kaizen: Continuously improving teacher using exponential moving average for semi‑supervised speech recognition. Proceedings of ASRU. Find / consult ↗
Mukherjee, S., & Awadallah, A. H. (2020). Uncertainty‑aware self‑training for few‑shot text classification. Advances in Neural Information Processing Systems, 33. Find / consult ↗
Mundnich, K., Niehues, J., et al. (2026). GLoRIA: Gated low‑rank interpretable adaptation for dialectal ASR. arXiv preprintarXiv:2603.02464.
Niculescu‑Mizil, A., & Caruana, R. (2005). Predicting good probabilities with supervised learning. Proceedings of ICML. Find / consult ↗
Olatunji, T., et al. (2025). Automatic speech recognition for African low‑resource languages: Challenges and future directions. Proceedings of AfricaNLP. Find / consult ↗
Ondel, L., Burget, L., & Černocký, J. (2017). Bayesian phonotactic language model for acoustic unit discovery. Proceedings of ICASSP. Find / consult ↗
Park, D. S., Zhang, Y., Jia, Y., Han, W., Chiu, C.‑C., Li, B., et al. (2020). Improved noisy student training for automatic speech recognition. Proceedings of Interspeech. Find / consult ↗
Park, D., Khurana, S., Berrebbi, D., et al. (2024). uDistil‑Whisper: Label‑free data filtering for knowledge distillation via large‑scale pseudo labelling. arXiv preprintarXiv:2407.01257.
Peddinti, V., Povey, D., & Khudanpur, S. (2015). A time delay neural network architecture for efficient modeling of long temporal contexts. Proceedings of Interspeech. Find / consult ↗
Povey, D., Peddinti, V., Galvez, D., Ghahremani, P., Manohar, V., Na, X., et al. (2016). Purely sequence‑trained neural networks for ASR based on lattice‑free MMI. Proceedings of Interspeech. Find / consult ↗
Prakash, J., Kumar, B., Hacioglu, K., Sharma, B., Gopalan, S., Chetlur, M., Venkatesan, S., & Stolcke, A. (2025). Better pseudo‑labeling with multi‑ASR fusion and error correction by SpeechLLM. Proceedings of Interspeech, 579–583. Find / consult ↗
Pratap, V., Tjandra, A., Shi, B., Tomasello, P., Babu, A., Kundu, S., et al. (2024). Scaling speech technology to 1 000+ languages (MMS). Journal of Machine Learning Research, 25, 1–52. Find / consult ↗
Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023). Robust speech recognition via large‑scale weak supervision (Whisper). Proceedings of ICML. Find / consult ↗
Rangappa, P., Carofilis, A., Prakash, J., Kumar, S., Burdisso, S., Madikeri, S., et al. (2025). Efficient data selection for domain adaptation of ASR using pseudo‑labels and multi‑stage filtering. Proceedings of Interspeech. arXiv:2506.03681.
Rizve, M. N., Duarte, K., Rawat, Y. S., & Shah, M. (2021). In defense of pseudo‑labeling: An uncertainty‑aware pseudo‑label selection framework for semi‑supervised learning. Proceedings of ICLR. Find / consult ↗
Rodemann, J., Goschenhofer, J., Dorigatti, E., Nagler, T., & Augustin, T. (2023). Approximately Bayes‑optimal pseudo‑label selection. Proceedings of UAI, PMLR 216. Find / consult ↗
Samdani, R., Chang, M.‑W., & Roth, D. (2012). Unified expectation maximization. Proceedings of NAACL‑HLT. Find / consult ↗
Schulman, J., Levine, S., Abbeel, P., Jordan, M., & Moritz, P. (2015). Trust region policy optimization. Proceedings of ICML. Find / consult ↗
Sohn, K., Berthelot, D., Li, C.‑L., Zhang, Z., Carlini, N., Cubuk, E. D., et al. (2020). FixMatch: Simplifying semi‑supervised learning with consistency and confidence. Advances in Neural Information Processing Systems, 33. Find / consult ↗
Tang, C., Yu, W., Sun, G., Chen, X., Tan, T., Li, W., et al. (2024). SALMONN: Towards generic hearing abilities for large language models. Proceedings of ICLR. Find / consult ↗
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. Find / consult ↗
Waheed, A., Talafha, B., Sajadi, A., Magdy, W., & Abdul‑Mageed, M. (2023). VoxArabica: A robust dialect‑aware Arabic speech recognition system. Proceedings of EMNLP (Demo). arXiv:2310.11069.
Wang, K., Wei, J., Lu, L., et al. (2023). Language‑routing mixture of experts for multilingual and code‑switching speech recognition. Proceedings of Interspeech. arXiv:2307.05956.
Wang, Y., Chen, H., Heng, Q., et al. (2023). FreeMatch: Self‑adaptive thresholding for semi‑supervised learning. Proceedings of ICLR. Find / consult ↗
Watanabe, S., Hori, T., Karita, S., Hayashi, T., Nishitoba, J., Unno, Y., et al. (2018). ESPnet: End‑to‑end speech processing toolkit. Proceedings of Interspeech. Find / consult ↗
Wei, C., Shen, K., Chen, Y., & Ma, T. (2021). Theoretical analysis of self‑training with deep networks on unlabeled data. Proceedings of ICLR. Find / consult ↗
Yan, B., Zhang, C., Yu, M., Zhang, S.‑X., Dalmia, S., Berrebbi, D., Weng, C., Watanabe, S., & Yu, D. (2021). Joint modeling of code‑switched and monolingual ASR via conditional factorization. arXiv preprintarXiv:2111.15016.
Yan, B., Pratap, V., Watanabe, S., & Auli, M. (2024). Improving multilingual ASR in the wild using simple N‑best re‑ranking. arXiv preprintarXiv:2409.18428.
Yang, X., Li, Q., Zhang, C., & Woodland, P. C. (2023). Knowledge distillation from multiple foundation models for end‑to‑end speech recognition. arXiv preprintarXiv:2303.10917.
Yu, Y., Zhang, X., Yang, C., et al. (2024). DialectMoE: An end‑to‑end multi‑dialect speech recognition model with mixture‑of‑experts. Proceedings of CCL. Find / consult ↗
Zhang, B., Wang, Y., Hou, W., Wu, H., et al. (2021). FlexMatch: Boosting semi‑supervised learning with curriculum pseudo labeling. Advances in Neural Information Processing Systems, 34. Find / consult ↗
Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., et al. (2023). Google USM: Scaling automatic speech recognition beyond 100 languages. arXiv preprintarXiv:2303.01037.
Zhao, J., et al. (2024). Boosting code‑switching ASR with mixture of experts enhanced speech‑conditioned LLM. arXiv preprintarXiv:2409.15905.
D
Theses and dissertations
Baevski, A. (2022). Self‑supervised learning of speech representations (Doctoral dissertation). New York University. Find thesis ↗
Likhomanenko, T. (2022). Self‑training for end‑to‑end speech recognition (Doctoral dissertation). Facebook AI Research / NYU. Find thesis ↗
Pratap, V. (2024). Scaling multilingual speech recognition to a thousand languages (Doctoral dissertation). MIT. Find thesis ↗
Synnaeve, G. (2014). Bayesian models for speech recognition and computer‑game artificial intelligence (Doctoral dissertation). University of Grenoble. Find thesis ↗
E
Standards and technical reports
International Phonetic Association. (2015). Handbook of the International Phonetic Association (Updated). Cambridge University Press. Find / consult ↗
ISO 639‑3. (2007). Codes for the representation of names of languages, Part 3: Alpha‑3 code for comprehensive coverage of languages. International Organization for Standardization. Find / consult ↗
National Institute of Standards and Technology. (2019). SCTK toolkit and the NIST RT evaluation series. NIST. Find / consult ↗
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. Find / consult ↗
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., et al. (2019). Model cards for model reporting. Proceedings of FAT*, 220–229. Find / consult ↗
Ardila, R., Branson, M., Davis, K., Henretty, M., Kohler, M., Meyer, J., et al. (2020). Common Voice: A massively multilingual speech corpus. Proceedings of LREC. Find / consult ↗
HuggingFace Inc. (2024). transformers: State‑of‑the‑art machine learning for PyTorch, TensorFlow and JAX [Software]. Find / consult ↗
Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., et al. (2011). The Kaldi speech recognition toolkit. Proceedings of ASRU. Find / consult ↗
Ramanantsoa, F. (2023). VoxMg: An automatic speech recognition dataset for Malagasy. 4th Workshop on African Natural Language Processing (AfricaNLP), ICLR. https://openreview.net/forum?id=zbAPDsBkGGb
Ratianantitra, V. M. (2023). A state‑of‑the‑art review on natural language processing applied to the Malagasy language. Proceedings of iCARTi. https://doi.org/10.59200/ICARTI.2023.001
Ratianantitra, V. M., et al. (2025). Towards robust Malagasy NLP: A novel corpus and evaluation framework for sentiment analysis. Language Resources and Evaluation. https://doi.org/10.1007/s10579-025-09900-w
SIL International. (2024). Ethnologue: Languages of the world. SIL. Find / consult ↗