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home / docs / papers / Modern Hebrew / VoxKnesset: A Large-Scale Longitudinal Hebrew Speech Dataset for Aging Speaker Modeling

VoxKnesset: A Large-Scale Longitudinal Hebrew Speech Dataset for Aging Speaker Modeling

What it is about

The authors release VoxKnesset, a speech corpus of about 2,300 hours from Israeli parliamentary plenary recordings (2009–2025). It covers 393 members of the Knesset, and many speak across up to 15 years. Each segment has word-level forced alignment from Whisper with a confidence score, and verified birth year and gender from official records. The speaker split is 80/20, so that all sessions of one speaker stay in one partition. The paper benchmarks WavLM-Large, ECAPA-TDNN and Wav2Vec2-XLSR-1B on age prediction and speaker verification. Age prediction gives an MAE of 6.3 years in-domain. Speaker-verification EER rises from 2.15% to 4.58% over 15 years for the strongest model. Cross-sectional age regressors do not follow within-speaker aging, but a longitudinal model recovers a usable signal. The paper is about speech, and Mova does not process audio.

Ideas

For Mova

I1 and I2 go into the corpus and test builder shared by the Hebrew crates (treebanks/he/ tooling, and the held-out rule in the release checks). I3 goes into 8. Lexicon and register as a register corpus. The speech models are out of scope.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv 2603.01270

@misc{lifshitz2026voxknesset,
  title = {{VoxKnesset: A Large-Scale Longitudinal Hebrew Speech Dataset for Aging Speaker Modeling}},
  author = {Lifshitz;, Yanir Marmor; Arad Zulti; David Krongauz; Adam Gabet; Yoad Snapir; Yair and Segal, Eran},
  year = {2026},
  eprint = {2603.01270},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2603.01270}
}

References

46 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. F. Kelly, N. Brümmer, and N. Harte, “Eigenageing compensation for speaker verification.” in INTERSPEECH, 2013, pp. 1624– 1628. find
  2. F. Kelly and J. H. L. Hansen, “Score-aging calibration for speaker verification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, pp. 2414–2424, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:3247309 ↗
  3. E. Dmitriev, Y. Kim, A. Matveeva, C. Montacié, Y. Boulard, Y. Sinyavskaya, Y. Zhukova, A. Zarazinski, E. Akhanov, I. I. Viksnin et al., “Locust-longitudinal corpus and toolset for speaker verification.” in INTERSPEECH, 2018, pp. 1096–1100. find
  4. D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust dnn embeddings for speaker recognition,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018, pp. 5329–5333. find
  5. J. H. L. Hansen and T. Hasan, “Speaker recognition by machines and humans: A tutorial review,” IEEE Signal Processing Magazine, vol. 32, no. 6, pp. 74–99, 2015. find
  6. W. Decoster and F. Debruyne, “Longitudinal voice changes: facts and interpretation,” Journal of Voice, vol. 14, no. 2, pp. 184–193, 2000. find
  7. J. Harrington, S. Palethorpe, C. I. Watson et al., “Age-related changes in fundamental frequency and formants: a longitudinal study of four speakers.” in Interspeech, vol. 2007, 2007, pp. 2753– 2756. find
  8. S. Rojas, E. Kefalianos, and A. Vogel, “How does our voice change as we age? a systematic review and meta-analysis of acoustic and perceptual voice data from healthy adults over 50 years of age,” Journal of Speech, Language, and Hearing Research, vol. 63, no. 2, pp. 533–551, 2020. find
  9. S. A. Xue and G. J. Hao, “Changes in the human vocal tract due to aging and the acoustic correlates of speech production,” Changes, 2003. find
  10. U. Reubold, J. Harrington, and F. Kleber, “Vocal aging effects on f0 and the first formant: A longitudinal analysis in adult speakers,” Speech communication, vol. 52, no. 7-8, pp. 638–651, 2010. find
  11. A. K. Jain, K. Nandakumar, and A. Ross, “50 years of biometric research: Accomplishments, challenges, and opportunities,” Pattern Recognition Letters, vol. 79, pp. 80–105, 2016. find
  12. M. Fairhurst, M. Erbilek, and M. Da Costa-Abreu, “Selective review and analysis of aging effects in biometric system implementation,” IEEE transactions on human-machine systems, vol. 45, no. 3, pp. 294–303, 2014. find
  13. F. Kelly, A. Drygajlo, and N. Harte, “Speaker verification with long-term ageing data,” in 2012 5th IAPR international conference on biometrics (ICB). IEEE, 2012, pp. 478–483. find
  14. X. Qin, N. Li, S. Duan, and M. Li, “Investigating long-term and short-term time-varying speaker verification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 3408–3423, 2024. find
  15. M. H. Bahari, M. McLaren, H. Van Hamme, and D. Van Leeuwen, “Age estimation from telephone speech using i-vectors,” in Proceedings of Interspeech 2012. ISCA, 2012, pp. 1171–1174. find
  16. F. Burkhardt, J. Wagner, H. Wierstorf, F. Eyben, and B. Schuller, “Speech-based age and gender prediction with transformers,” in Speech Communication; 15th ITG Conference. VDE, 2023, pp. 46–50. find
  17. D. Alharthi, M. Zamani, B. Raj, and R. Singh, “Tessellated linear model for age prediction from voice,” in ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–4. find
  18. A. Tursunov, Mustaqeem, J. Y. Choeh, and S. Kwon, “Age and gender recognition using a convolutional neural network with a specially designed multi-attention module through speech spectrograms,” Sensors, vol. 21, no. 17, p. 5892, 2021. find
  19. D. Kwasny and D. Hemmerling, “Gender and age estimation methods based on speech using deep neural networks,” Sensors, vol. 21, no. 14, p. 4785, 2021. find
  20. J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, D. S. Pallett, and N. L. Dahlgren, “Darpa timit acoustic-phonetic continuous speech corpus,” Linguistic Data Consortium, Philadelphia, 1993, lDC93S1. find
  21. R. A. Cole, M. Noel, and V. Noel, “The cslu speaker recognition corpus.” in ICSLP, vol. 98, 1998, pp. 3167–3170. find
  22. A. D. Lawson, A. Stauffer, E. J. Cupples, S. J. Wenndt, W. Bray, and J. J. Grieco, “The multi-session audio research project (marp) corpus: Goals, design and initial findings,” Tech. Rep., 2009. find
  23. L. Brandschain, D. Graff, C. Cieri, K. Walker, C. Caruso, and A. Neely, “Greybeard longitudinal speech study,” in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), 2010. find
  24. J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in Proceedings of Interspeech 2018. ISCA, 2018, pp. 1086–1090. find
  25. N. Tawara, A. Ogawa, Y. Kitagishi, and H. Kamiyama, “Age-voxceleb: Multi-modal corpus for facial and speech estimation,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 6963– 6967. find
  26. D. Krongauz, H. Pinto, S. Kohn, Y. Marmor, and E. Segal, “Hpp-voice: A large-scale evaluation of speech embeddings for multi-phenotypic classification,” arXiv preprint arXiv:2505.16490, 2025. ↗
  27. Z. Ai, M. Bao, Z. Chen, Z. Yang, X. Li, and S. Xu, “Voxaging: Continuously tracking speaker aging with a large-scale longitudinal dataset in english and mandarin,” in Proc. Interspeech 2025, 2025, pp. 3648–3652. find
  28. T. Erjavec, M. Ogrodniczuk, P. Osenova, N. Ljubešić, K. Simov, A. Pančur, M. Rudolf, M. Kopp, S. Barkarson, S. Steingrı́msson et al., “The parlamint corpora of parliamentary proceedings,” Language resources and evaluation, vol. 57, no. 1, pp. 415–448, 2023. find
  29. T. Erjavec, M. Kopp, N. Ljubešić, T. Kuzman, P. Rayson, P. Osenova, M. Ogrodniczuk, Ç. Çöltekin, D. Koržinek, K. Meden et al., “Parlamint ii: advancing comparable parliamentary corpora across europe: T. erjavec et al.” Language Resources and Evaluation, vol. 59, no. 3, pp. 2071–2102, 2025. find
  30. N. Ljubešić, D. Koržinek, P. Rupnik, and I.-P. Jazbec, “Parlaspeech-hr-a freely available asr dataset for croatian bootstrapped from the parlamint corpus,” in Proceedings of the workshop ParlaCLARIN III within the 13th language resources and evaluation Conference, 2022, pp. 111–116. find
  31. C. Wang, M. Riviere, A. Lee, A. Wu, C. Talnikar, D. Haziza, M. Williamson, J. Pino, and E. Dupoux, “Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021, pp. 993–1003. find
  32. A. Virkkunen, A. Rouhe, N. Phan, and M. Kurimo, “Finnish parliament asr corpus: Analysis, benchmarks and statistics,” Language Resources and Evaluation, vol. 57, no. 4, pp. 1645–1670, 2023. find
  33. A. Itai and S. Wintner, “Language resources for hebrew,” Language Resources and Evaluation, vol. 42, no. 1, pp. 75–98, 2008. find
  34. Y. Marmor, K. Misgav, and Y. Lifshitz, “ivrit. ai: A comprehensive dataset of hebrew speech for ai research and development,” arXiv preprint arXiv:2307.08720, 2023. ↗ our card
  35. A. Turetzky, O. Tal, Y. Segal, Y. Dissen, E. Zeldes, A. Roth, E. Cohen, Y. Shrem, B. R. Chernyak, O. Seleznova et al., “Hebdb: a weakly supervised dataset for hebrew speech processing,” in Proc. Interspeech 2024, 2024, pp. 1360–1364. find our card
  36. O. Sharoni, R. Shenberg, and E. Cooper, “Saspeech: A hebrew single speaker dataset for text to speech and voice conversion.” in INTERSPEECH, 2023, pp. 5566–5570. find
  37. Y. Marmor, Y. Lifshitz, Y. Snapir, and K. Misgav, “Building an accurate open-source hebrew asr system through crowdsourcing,” in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2025, pp. 723–727. find
  38. B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,” in Proceedings of Interspeech 2020. ISCA, 2020, pp. 3830–3834. find
  39. A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020, pp. 12 449–12 460. find
  40. A. Babu, C. Wang, A. Tjandra, K. Lakhotia, Q. Xu, N. Goyal, K. Singh, P. Von Platen, Y. Saraf, J. Pino et al., “Xls-r: Self-supervised cross-lingual speech representation learning at scale,” arXiv preprint arXiv:2111.09296, 2021. ↗
  41. S. Chen et al., “Wavlm: Large-scale self-supervised pre-training for full stack speech processing,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 6, pp. 1505–1520, 2022. find
  42. A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in International conference on machine learning. PMLR, 2023, pp. 28 492–28 518. find
  43. jianfch, “stable-ts: Transcription, forced alignment, and audio indexing with openai’s whisper,” https://github.com/ jianfch/stable-ts, 2023, gitHub repository, accessed 2026-02-24. [Online]. Available: https://github.com/jianfch/stable-ts ↗
  44. G. Goldin, N. Howell, N. Ordan, E. Rabinovich, and S. Wintner, “The knesset corpus: an annotated corpus of hebrew parliamentary proceedings,” Language Resources and Evaluation, vol. 59, no. 3, pp. 2973–3004, 2025. find our card
  45. Y. Chen, S. Zheng, H. Wang, L. Cheng, Q. Chen, and J. Qi, “An enhanced res2net with local and global feature fusion for speaker verification,” arXiv preprint arXiv:2305.12838, 2023. ↗
  46. Y. Chen, S. Zheng, H. Wang, L. Cheng, Q. Chen, C. Deng, S. Zhang, and W. Wang, “Self-distillation prototypes network: Learning robust speaker representations without supervision,” in ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–5. find

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