The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild
Open accessAbstract
Obtaining large, human labelled speech datasets to train models for emotion recognition is a notoriously challenging task, hindered by annotation cost and label ambiguity. In this work, we consider the task of learning embeddings for speech classification without access to any form of labelled audio. We base our approach on a simple hypothesis: that the emotional content of speech correlates with the facial expression of the speaker. By exploiting this relationship, we show that annotations of expression can be transferred from the visual domain (faces) to the speech domain (voices) through cross-modal distillation. We make the following contributions: (i) we develop a strong teacher network for facial emotion recognition that achieves the state of the art on a standard benchmark; (ii) we use the teacher to train a student, tabula rasa, to learn representations (embeddings) for speech emotion recognition without access to labelled audio data; and (iii) we show that the speech emotion embedding can be used for speech emotion recognition on external benchmark datasets. Code, models and data are available.
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Citations
Cites
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Self-citationVGGFace2: A Dataset for Recognising Faces across Pose and Age
Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00020 · Cited by 3,032
Self-citationUnsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629
A database of German emotional speech
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-446 · Cited by 2,365
VoxCeleb: A Large-Scale Speaker Identification Dataset
Nagrani, A., Chung, J. S., & Zisserman, A. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-950 · Cited by 2,210
Self-citationVisual Following and Pattern Discrimination of Face-like Stimuli by Newborn Infants
Goren, C. C., Sarty, M., & Wu, P. Y. K. (1975) · Pediatrics · doi:10.1542/peds.56.4.544 · Cited by 1,158 · cited 1×
“e source of supervision that infants use as their visual and audio capabilities develop [30]. Newborns look longer at face-like stimuli and track them farther than non-face-like stimuli (Goren et al. [29]; Johnson et al. [38]), and combining these facial stimuli together with voices, detect information that later may…”
BackgroundAnalysis of emotion recognition using facial expressions, speech and multimodal information
Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004) · Proceedings of the 6th international conference on Multimodal interfaces · doi:10.1145/1027933.1027968 · Cited by 840 · cited 2×
“Second, it has been observed that certain emotions are easier to detect from speech than faces, and vice versa [15], suggesting that the degree to which there is a redundant emotional signal across modalities may differ across emotions.”
BackgroundOut of Time: Automated Lip Sync in the Wild
Chung, J. S., & Zisserman, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54427-4_19 · Cited by 689
Self-citationCross Modal Distillation for Supervision Transfer
Gupta, S., Hoffman, J., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.309 · Cited by 530 · cited 3×
“The objective of this work is to learn useful representations for emotion speech recognition, without access to labelled speech data. Our approach, inspired by the method of cross modal distillation [31], is to tackle this problem by exploiting readily available annotated data in the visual domain. Under the formulati…”
BackgroundMethodInfluential
Cited by
Knowledge Distillation: A Survey
Gou, J., Yu, B., Maybank, S. J., & Tao, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01453-z · Cited by 3,931
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Wang, K., Peng, X., Yang, J., Lu, S., & Qiao, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00693 · Cited by 772
Learnable PINs: cross-modal embeddings for person identity
Nagrani, A., Albanie, S., & Zisserman, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_5 · Cited by 151
Self-citationFederated Contrastive Learning With Feature-Based Distillation for Human Activity Recognition
Xiao, Z., & Tong, H. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3510428 · Cited by 86
Sun, L., Lian, Z., Tao, J., Liu, B., & Niu, M. (2020) · Proceedings of the 1st International on Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop · doi:10.1145/3423327.3423672 · Cited by 81
Semi-supervised Multi-modal Emotion Recognition with Cross-Modal Distribution Matching
Liang, J., Li, R., & Jin, Q. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413579 · Cited by 73
Multimodal Emotion Recognition and Sentiment Analysis via Attention Enhanced Recurrent Model
Sun, L., Xu, M., Lian, Z., Liu, B., Tao, J., Wang, M., & Cheng, Y. (2021) · Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge · doi:10.1145/3475957.3484456 · Cited by 36
Hierarchical fusion of visual and physiological signals for emotion recognition
Fang, Y., Rong, R., & Huang, J. (2021) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-021-00774-z · Cited by 17
Metadata
- Authors
- Albanie, S., Nagrani, A., Vedaldi, A., & Zisserman, A.
- Venue
- Proceedings of the 26th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2018-10-15
- Pages
- 292-301
- OpenAlex
- W2886300652
- Cited by
- 290
- References
- 12
- FWCI
- 42.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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