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

Yanir Marmor; Arad Zulti; David Krongauz; Adam Gabet; Yoad Snapir; Yair Lifshitz; Eran Segal

eess.ASMar 1, 2026

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

Authors: Yanir Marmor, Arad Zulti, David Krongauz, Adam Gabet, Yoad Snapir, Yair Lifshitz, Eran Segal

Organizations: Department of Computer Science and Applied Mathematics, Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

Abstract

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.

Figures & tables

Figure 1: VoxKnesset pairs single-speaker speech segments with aligned transcripts and verified demographic labels across 16 years of parliamentary recordings.

Figure 2: Longitudinal coverage in VoxKnesset by sex. Bars show the age range per recording year; white markers indicate median age; color intensity reflects the number of unique speakers (annotated).

Figure 3: Data curation pipeline: from raw parliamentary recordings to the speaker-attributed longitudinal subset.

Figure 4: Age distributions across commonly used speech datasets, illustrating differences in demographic coverage.

Task	#C	AUC	Maj.
Gender	2	1.00	.86
Religion	4	.95	.95
Birthplace	6	.73	.85

Table 1: Demographic prediction with WavLM-L embeddings (Ridge). #C = classes; Maj. = majority baseline.

Dataset	WavLM	ECAPA	XLS-R	WavLM	$\Delta R^{2}$
	Within-dataset			LODO
VoxKnesset	0.42 / 6.3	0.24 / 7.2	0.24 / 7.5	0.33 / 7.6	0.09
TIMIT	0.43 / 4.6	0.27 / 5.2	0.14 / 5.6	$-$ 2.52 / 14.2	2.95
AgeVoxCeleb	0.57 / 7.3	0.42 / 8.6	0.29 / 9.5	0.17 / 10.2	0.40
HPP-Voice	0.56 / 5.4	0.38 / 6.5	0.35 / 6.5	0.01 / 8.7	0.55

Table 2: Age prediction (

R^{2}

/MAE in years) within-domain and under leave-one-dataset-out (LODO) cross-corpus transfer, using WavLM-Large for transfer.

\Delta R^{2}

measures the in-domain vs. transfer gap.

Panel of Figure 5 — Figure 5: Longitudinal effects of speaker aging: (a) Embedding space distribution, (b) age prediction accuracy, and (c) verification performance degradation.

Speech Processing

Speech processing develops computational methods for recognizing, synthesizing, analyzing, and transforming spoken audio.

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VieSpeaker: A Large-Scale Vietnamese Speaker Recognition Dataset Beyond Visual Dependency

Viet Hoang Pham, Tran Trung Nguyen, Bao Thu Ho, Phuong Tuan Dat+1

Hanoi University of Science and Technology, Hanoi, Vietnam

Speech Processing Low-Resource Language Processing

Open paper AskPDF arXiv

Speaker recognition has advanced rapidly with large-scale training datasets, yet Vietnamese remains under-resourced, with existing corpora limited in scale and acoustic diversity. Most large-scale datasets rely on facial cues to link speech with speaker identities, restricting data collection to recordings where speakers appear on camera. We propose a face-independent dataset construction pipeline and introduce VieSpeaker, a large-scale Vietnamese speaker recognition dataset. Our approach leverages textual metadata and large language model reasoning to infer speaker identities from transcripts and contextual information. VieSpeaker contains approximately 902 hours of speech from 4,715 speakers. Experiments show that models trained on VieSpeaker achieve improved robustness and generalization compared to existing Vietnamese datasets. This work demonstrates the feasibility of face-independent dataset construction and provides a new direction for building large-scale speech resources.

Abstract

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