SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors
Yang Chen, Hui Wang, Shiyao Wang, Junyang Chen, Jiabei He, Jiaming Zhou, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
TL;DR
The paper addresses the lack of Mandarin speech data for the super-aged (75+) and its impact on voice technologies. It introduces SeniorTalk, a freely available corpus totaling $55.53$ hours from $202$ participants across $16$ provinces, with $60{,}029$ utterances and rich multi-dimensional annotations designed for multiple tasks. Extensive experiments across $SV$, $SD$, $ASR$, and $speech editing$ establish SeniorTalk as a valuable benchmark for aging-voice research, showing domain adaptation benefits for elderly-specific models (e.g., Paraformer and ECAPA-TDNN variants) and highlighting gaps between elderly and general-domain training data. By open-sourcing the dataset with detailed metadata and ethical safeguards, the work aims to accelerate robust, equitable speech technologies for aging populations.
Abstract
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.