The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Recent advances in the automatic recognition of audiovisual speech
Abstract
Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition (ASR) and present novel contributions in two main areas: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves ASR over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks.
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Cites
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Neural network-based face detection
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“The visual modality benefit to speech intelligibility in noise has been quantified as far back as in 1954 [6].”
BackgroundMaximum mutual information estimation of hidden Markov model parameters for speech recognition
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LIPS, TEETH, AND THE BENEFITS OF LIPREADING
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Cited by
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
Multimodal fusion for multimedia analysis: a survey
Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010) · Multimedia Systems · doi:10.1007/s00530-010-0182-0 · Cited by 1,279
Self-supervised speech representation learning: A review
Mohamed, A., Lee, H.-Y., Borgholt, L., Havtorn, J. D., Edin, J., Igel, C., Kirchhoff, K., Li, S.-W., Livescu, K., Maaløe, L., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3207050 · Cited by 372
“Multi-modal work includes supervised settings, such as audio-visual ASR [143], [144] and person identification [145] which have been studied for decades.”
BackgroundCombining Residual Networks with LSTMs for Lipreading
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MULTIMODAL EMOTION RECOGNITION
Sebe, N., Cohen, I., & Huang, T. S. (2005) · Handbook of Pattern Recognition and Computer Vision · doi:10.1142/9789812775320_0021 · Cited by 201
Metadata
- Authors
- Pomianos, G., Neti, C., Gravier, G., Garg, A., & Senior, A. W.
- Venue
- Proceedings of the IEEE
- Published
- 2003-09
- Volume
- 91
- Issue
- 9
- Pages
- 1306-1326
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2096391593
- Cited by
- 752
- References
- 6
- FWCI
- 38.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pomianos, G., Neti, C., Gravier, G., Garg, A., & Senior, A. W. (2003). Recent advances in the automatic recognition of audiovisual speech. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2003.817150
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