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

arXiv: https://arxiv.org/abs/2603.01270 · HTML: https://mlsanity.com/paper/2603.01270/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification](https://mlsanity.com/paper/2305.12838/) (2023)
- [Speech-based Age and Gender Prediction with Transformers](https://mlsanity.com/paper/2306.16962/) (2023)
- [Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision](https://mlsanity.com/paper/2406.11169/) (2024)
- [ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development](https://mlsanity.com/paper/2307.08720/) (2023)
- [The Knesset Corpus: An Annotated Corpus of Hebrew Parliamentary Proceedings](https://mlsanity.com/paper/2405.18115/) (2024)
- [HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing](https://mlsanity.com/paper/2407.07566/) (2024)
- [Tessellated Linear Model for Age Prediction from Voice](https://mlsanity.com/paper/2501.09229/) (2025)
- [HPP-Voice: A Large-Scale Evaluation of Speech Embeddings for Multi-Phenotypic Classification](https://mlsanity.com/paper/2505.16490/) (2025)
- [VoxAging: Continuously Tracking Speaker Aging with a Large-Scale Longitudinal Dataset in English and Mandarin](https://mlsanity.com/paper/2505.21445/) (2025)

## Built on by (most cited first, 1 of 1)

- [ReNikud: Audio-Supervised Hebrew Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/2606.20179/) (2026)

## Abstract (arXiv)

Speech processing systems face a fundamental challenge: the human voice changes with age, yet few datasets support rigorous longitudinal evaluation. We introduce VoxKnesset, an open-access dataset of ~2,300 hours of Hebrew parliamentary speech spanning 2009-2025, comprising 393 speakers with recording spans of up to 15 years. Each segment includes aligned transcripts and verified demographic metadata from official parliamentary records. We benchmark modern speech embeddings (WavLM-Large, ECAPA-TDNN, Wav2Vec2-XLSR-1B) on age prediction and speaker verification under longitudinal conditions. Speaker verification EER rises from 2.15% to 4.58% over 15 years for the strongest model, and cross-sectionally trained age regressors fail to capture within-speaker aging, while longitudinally trained models recover a meaningful temporal signal. We publicly release the dataset and pipeline to support aging-robust speech systems and Hebrew speech processing.