WeCanTalk: A New Multi-language, Multi-modal Resource for Speaker Recognition
Karen Jones, Kevin Walker, Christopher Caruso, Jonathan Wright, Stephanie Strassel
Abstract
The WeCanTalk (WCT) Corpus is a new multi-language, multi-modal resource for speaker recognition. The corpus contains Cantonese, Mandarin and English telephony and video speech data from over 200 multilingual speakers located in Hong Kong. Each speaker contributed at least 10 telephone conversations of 8-10 minutes’ duration collected via a custom telephone platform based in Hong Kong. Speakers also uploaded at least 3 videos in which they were both speaking and visible, along with one selfie image. At least half of the calls and videos for each speaker were in Cantonese, while their remaining recordings featured one or more different languages. Both calls and videos were made in a variety of noise conditions. All speech and video recordings were audited by experienced multilingual annotators for quality including presence of the expected language and for speaker identity. The WeCanTalk Corpus has been used to support the NIST 2021 Speaker Recognition Evaluation and will be published in the LDC catalog.- Anthology ID:
- 2022.lrec-1.369
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 3451–3456
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.369
- DOI:
- Cite (ACL):
- Karen Jones, Kevin Walker, Christopher Caruso, Jonathan Wright, and Stephanie Strassel. 2022. WeCanTalk: A New Multi-language, Multi-modal Resource for Speaker Recognition. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3451–3456, Marseille, France. European Language Resources Association.
- Cite (Informal):
- WeCanTalk: A New Multi-language, Multi-modal Resource for Speaker Recognition (Jones et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.lrec-1.369.pdf