2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-46.4
Paper Title AGE-VOX-CELEB: MULTI-MODAL CORPUS FOR FACIAL AND SPEECH ESTIMATION
Authors Naohiro Tawara, Atsunori Ogawa, Yuki Kitagishi, Hosana Kamiyama, NTT Corporation, Japan
SessionSPE-46: Corpora and Other Resources
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Speech Processing: [SPE-GASR] General Topics in Speech Recognition
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Estimating a speaker's age from her speech is more challenging than age estimation from her face because of insufficiently available public corpora. To tackle this problem, we construct a new audio-visual age corpus named {\it AgeVoxCeleb} by annotating age labels to VoxCeleb2 videos. AgeVoxCeleb is the first large-scale, balanced, and multi-modal age corpus that contains both video and speech of the same speakers from a wide age range. Using AgeVoxCeleb, our paper makes the following contributions: (i) A facial age estimation model can outperform a speech age estimation model by comparing the state-of-the-art models in each task. (ii) Facial age estimation is more robust against the difference between training and test sets. (iii) We developed cross-modal transfer learning from face to speech age estimation, showing that the estimated age with a facial age estimation model can be used to train a speech age estimation model. Proposed AgeVoxCeleb will be published in https://github.com/nttcslab-sp/agevoxceleb.