SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors
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Abstract (arXiv)
While voice technologies increasingly serve aging populations, current systems exhibit significant performance gaps due to inadequate training data capturing elderly-specific vocal characteristics like presbyphonia and dialectal variations. The limited data available on super-aged individuals in existing elderly speech datasets, coupled with overly simple recording styles and annotation dimensions, exacerbates this issue. To address the critical scarcity of speech data from individuals aged 75 and above, we introduce SeniorTalk, a carefully annotated Chinese spoken dialogue dataset. This dataset contains 55.53 hours of speech from 101 natural conversations involving 202 participants, ensuring a strategic balance across gender, region, and age. Through detailed annotation across multiple dimensions, it can support a wide range of speech tasks. We perform extensive experiments on speaker verification, speaker diarization, speech recognition, and speech editing tasks, offering crucial insights for the development of speech technologies targeting this age group.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 20 Mar 2025.
Builds on
21 of 46 references.
References · 21
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9 more
- 2022A3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editingcited by 40
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.