The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Extraction of visual features for lipreading
Abstract
The multimodal nature of speech is often ignored in human-computer interaction, but lip deformations and other body motion, such as those of the head, convey additional information. We integrate speech cues from many sources and this improves intelligibility, especially when the acoustic signal is degraded. The paper shows how this additional, often complementary, visual speech information can be used for speech recognition. Three methods for parameterizing lip image sequences for recognition using hidden Markov models are compared. Two of these are top-down approaches that fit a model of the inner and outer lip contours and derive lipreading features from a principal component analysis of shape or shape and appearance, respectively. The third, bottom-up, method uses a nonlinear scale-space analysis to form features directly from the pixel intensity. All methods are compared on a multitalker visual speech recognition task of isolated letters.
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Cites
Hearing lips and seeing voices
MCGURK, H., & MACDONALD, J. (1976) · Nature · doi:10.1038/264746a0 · Cited by 6,183
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Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Self-citationVisual Contribution to Speech Intelligibility in Noise
Sumby, W. H., & Pollack, I. (1954) · The Journal of the Acoustical Society of America · doi:10.1121/1.1907309 · Cited by 2,583 · cited 1×
“Two of these are top-down approaches that fit a model of the inner and outer lip contours and derive lipreading features from a principal component analysis of shape or shape and appearance, respectively.”
BackgroundKoenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452
Integration of acoustic and visual speech signals using neural networks
Yuhas, B. P., Goldstein, M. H., & Sejnowski, T. J. (1989) · IEEE Communications Magazine · doi:10.1109/35.41402 · Cited by 209
Cited by
(2011) · Cited by 2,752
Chung, J. S., & Zisserman, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54184-6_6 · Cited by 537
Data Fusion and Multicue Data Matching by Diffusion Maps
Lafon, S., Keller, Y., & Coifman, R. R. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.223 · Cited by 312
Temporal Multimodal Learning in Audiovisual Speech Recognition
Hu, D., Li, X., & Lu, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.389 · Cited by 108
Deep learning for visual speech analysis: A survey
Sheng, C., Kuang, G., Bai, L., Hou, C., Guo, Y., Xu, X., Pietikäinen, M., & Liu, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3376710 · Cited by 64
Metadata
- Authors
- Matthews, I., Cootes, T. F., Bangham, J. A., Cox, S., & Harvey, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002
- Volume
- 24
- Issue
- 2
- Pages
- 198-213
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2113814270
- Cited by
- 537
- References
- 6
- FWCI
- 20.90
- Type
- journal article
- Language
- en
- Open access
- closed
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