SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors
This addresses the problem of inadequate voice technologies for elderly populations, specifically those aged 75 and above, by providing a richly annotated dataset, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of speech data for super-aged seniors by introducing SeniorTalk, a Chinese conversation dataset with 55.53 hours of speech from 101 conversations, which improved performance on tasks like speaker verification and speech recognition.
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.