VoxKnesset: A Large-Scale Longitudinal Hebrew Speech Dataset for Aging Speaker Modeling
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of VoxKnesset, a large-scale longitudinal Hebrew speech dataset that enables the study of aging effects on speech, along with a comprehensive evaluation of modern speech embeddings in this context. This work represents a significant advancement in the field of speech processing, particularly for underrepresented languages and demographic studies.
Methodology
The methodology employed in VoxKnesset is robust and well-structured, focusing on the creation of a large-scale longitudinal speech dataset specifically for Hebrew parliamentary speech. The authors detail a multi-stage alignment pipeline that addresses common issues in audio processing, such as timestamp inconsistencies and transcript normalization artifacts. The use of verified demographic metadata enhances the dataset's reliability. The longitudinal aspect of the dataset is particularly noteworthy, as it allows for the examination of vocal changes over time, a significant advancement over traditional cross-sectional datasets. The benchmarking of modern speech embeddings on age prediction and speaker verification is methodologically sound, providing a clear framework for evaluating the impact of aging on speech characteristics.
Results & evaluation
The experiments conducted are thorough and well-articulated, demonstrating the dataset's utility in real-world applications. The authors benchmark several state-of-the-art speech embeddings, providing a comprehensive analysis of their performance in both age prediction and speaker verification tasks. The results clearly show the degradation of speaker verification performance over time, highlighting the importance of longitudinal data in understanding vocal aging. The cross-dataset evaluations further validate the dataset's applicability and the robustness of the findings across different languages and contexts. The use of various metrics, including Mean Absolute Error (MAE) and Equal Error Rate (EER), adds depth to the evaluation.
Reproducibility
While the paper mentions that the dataset and processing pipeline will be publicly released, specific implementation details are somewhat lacking. The authors provide a general overview of their methods, but more granular details about the experimental setup, hyperparameters, and the exact processing steps would enhance reproducibility. Clear documentation and access to code would be beneficial for other researchers looking to replicate or build upon this work.
Limitations
The paper acknowledges several limitations, including the dataset's focus on a single speech register (parliamentary debate) and a demographic skew towards older adults. Additionally, the authors note that recording conditions may have evolved over the 16 years, which could introduce confounding variables. The challenge of disentangling channel drift from biological aging is also recognized, indicating that further research is needed to fully understand these dynamics.
Broader impact
The VoxKnesset dataset has significant implications for various applications, including biometric security, automated transcription, and health diagnostics. By addressing the aging of vocal characteristics, this work could lead to more robust and reliable speech processing systems that can adapt to individual changes over time. The dataset's release will likely stimulate further research in Hebrew speech processing and aging-related studies, contributing to the broader field of machine learning and speech technology. The main contribution of this paper is the introduction of VoxKnesset, a large-scale longitudinal Hebrew speech dataset that enables the study of aging effects on speech, along with a comprehensive evaluation of modern speech embeddings in this context. This work represents a significant advancement in the field of speech processing, particularly for underrepresented languages and demographic studies.
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