The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
The AMI Meeting Corpus: A Pre-announcement
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Cites
Engineering Design: A Systematic Approach
(1963) · Students Quarterly Journal · doi:10.1049/sqj.1963.0055 · Cited by 4,100
Anderson, A. H., Bader, M., Bard, E. G., Boyle, E., Doherty, G., Garrod, S., Isard, S., Kowtko, J., McAllister, J., Miller, J., et al. (1991) · Language and Speech · doi:10.1177/002383099103400404 · Cited by 1,017
Pahl, G., & Beitz, W. (1996) · Springer eBooks · doi:10.1007/978-1-4471-3581-4 · Cited by 890
FEELTRACE: an instrument for recording perceived emotion in real time
Cowie, R., Douglas‐Cowie, E., Savvidou, S. M., & McMahon, E. R. (2000) · Research Portal (Queen's University Belfast) · Cited by 539
Speaking in shorthand – A syllable-centric perspective for understanding pronunciation variation
Greenberg, S. (1999) · Speech Communication · doi:10.1016/s0167-6393(99)00050-3 · Cited by 353
Text detection and recognition in images and video frames
Chen, D., Odobez, J.-M., & Bourlard, H. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2003.06.001 · Cited by 277
The NITE XML Toolkit: Flexible annotation for multimodal language data
Carletta, J., Evert, S., Heid, U., Kilgour, J., Robertson, J., & Voormann, H. (2003) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03195511 · Cited by 119
Self-citationHandwriting Recognition of Whiteboard Notes
Liwicki, M., & Bunke, H. (2005) · Cited by 31
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
Ego4D: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630
SpeechBrain: A General-Purpose Speech Toolkit
Ravanelli, M., Parcollet, T., Plantinga, P., Rouhe, A., Cornell, S., Lugosch, L., Subakan, C., Dawalatabad, N., Heba, A., Zhong, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.04624 · Cited by 512
A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
Speaker recognition based on deep learning: An overview
Bai, Z., & Zhang, X.-L. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.004 · Cited by 386
Metadata
- Authors
- Carletta, J., Ashby, S., Bourban, S., Flynn, M., Guillemot, M., Hain, T., Kadlec, J., Karaiskos, V., Kraaij, W., Kronenthal, M., Lathoud, G., Lincoln, M., Lisowska, A., McCowan, I., Post, W., Reidsma, D., & Wellner, P.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 28-39
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2125336414
- Cited by
- 908
- References
- 8
- FWCI
- 27.37
- Type
- conference paper
- Language
- en
- Open access
- green
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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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.
