The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Quantitative association of vocal-tract and facial behavior
Open accessAbstract
This paper examines the degrees of correlation among vocal-tract and facial movement data and the speech acoustics. Multilinear techniques are applied to support the claims that facial motion during speech is largely a by-product of producing the speech acoustics and further that the spectral envelope of the speech acoustics can be better estimated by the 3D motion of the face than by the midsagittal motion of the anterior vocal-tract (lips, tongue and jaw). Experimental data include measurements of the motion of markers placed on the face and in the vocal-tract, as well as the speech acoustics, for two subjects. The numerical results obtained show that, for both subjects, 91% of the total variance observed in the facial motion data could be determined from vocal-tract motion by means of simple linear estimators. For the inverse path, i.e. recovery of vocal-tract motion from facial motion, the results indicate that about 80% of the variance observed in the vocal-tract can be estimated from the face. Regarding the speech acoustics, it is observed that, in spite of the nonlinear relation between vocal-tract geometry and acoustics, linear estimators are sufficient to determine between 72 and 85% (depending on subject and utterance) of the variance observed in the RMS amplitude and LSP parametric representation of the spectral envelope. A dimensionality analysis is also carried out, and shows that between four and eight components are sufficient to represent the mappings examined. Finally, it is shown that even the tongue, which is an articulator not necessarily coupled with the face, can be recovered reasonably well from facial motion since it frequently displays the same kind of temporal pattern as the jaw during speech.
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Cites
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Electromagnetic midsagittal articulometer systems for transducing speech articulatory movements
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Chandrasekaran, C., Trubanova, A., Stillittano, S., Caplier, A., & Ghazanfar, A. A. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000436 · Cited by 630
Audio-visual speech recognition using deep learning
Noda, K., Yamaguchi, Y., Nakadai, K., Okuno, H. G., & Ogata, T. (2015) · Applied Intelligence · doi:10.1007/s10489-014-0629-7 · Cited by 579
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Grant, K. W., & Seitz, P.-F. (2000) · The Journal of the Acoustical Society of America · doi:10.1121/1.1288668 · Cited by 521
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Munhall, K. G., Jones, J. A., Callan, D. E., Kuratate, T., & Vatikiotis-Bateson, E. (2004) · Psychological Science · doi:10.1111/j.0963-7214.2004.01502010.x · Cited by 482
Self-citationAn Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
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Metadata
- Authors
- Yehia, H., Rubin, P., & Vatikiotis-Bateson, E.
- Venue
- Speech Communication
- Publisher
- Elsevier BV
- Published
- 1998-10
- Volume
- 26
- Issue
- 1-2
- Pages
- 23-43
- ISSN
- 0167-6393 (ISSN-L)
- OpenAlex
- W2127211243
- Cited by
- 420
- References
- 26
- FWCI
- 10.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.