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Computationally Efficient Clustering of Audio-Visual ...
https://www1.icsi.berkeley.edu/~fractor/papers/friedland_137.pdf
Computationally Efficient Clustering of Audio-Visual Meeting Data Hayley Hung, Gerald Friedland, and Chuohao Yeo Abstract This chapter presents novel computationally efficient algorithms to ex- tract semantically meaningful acoustic and visual events related to …
Chapter 1 Computationally Efficient Clustering of Audio ...
https://homepage.tudelft.nl/3e2t5/Hungetal_BookChap2010.pdf
1 Computationally Efficient Clustering of Audio-Visual Meeting Data 3 the natural choice if we wish to extract the verbal content of what is being said so that interactionscanbe analyzedsemantically.However,while semantics are closely related to verbal cues, meaning can also be extracted from non-verbal features. In
Computationally Efficient Clustering of Audio-Visual ...
https://link.springer.com/chapter/10.1007/978-1-84996-507-1_2
The recording setup involves relatively few audio-visual sensors, comprising a limited number of cameras and microphones. We first demonstrate computationally efficient algorithms that can identify who spoke and when, a problem in speech processing known as speaker diarization.
(PDF) Computationally Efficient Clustering of Audio …
https://www.researchgate.net/publication/226214892_Computationally_Efficient_Clustering_of_Audio-Visual_Meeting_Data
Computationally Efficient Clustering of Audio-Visual Meeting Data ... This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and …
Chapter 1 Computationally Efficient Clustering of Audio ...
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.700.4778
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the partici-pants in a group discussion using the example of business meeting recordings. The recording set-up involves relatively few audio …
Computationally Efficient Clustering of Audio-Visual ...
https://ui.adsabs.harvard.edu/abs/2010miiu.book...25H/abstract
This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the participants in a group discussion using the example of business meeting recordings. The recording setup involves relatively few audio-visual sensors, comprising a limited number of cameras and microphones.
Computationally efficient clustering of audio-visual ...
https://www.narcis.nl/publication/RecordID/oai%3Adare.uva.nl%3Apublications%2Fed6a01e6-c9f9-4c75-a925-07fa4a4d12cb
Chapter 1 Computationally Efficient Clustering of Audio ...
https://core.ac.uk/display/103472048
Chapter 1 Computationally Efficient Clustering of Audio-Visual Meeting Data . By ... Abstract This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the partici-pants in a group discussion using the example of business meeting recordings. ... we present a ...
(PDF) Associating Audio-Visual Activity Cues in a ...
https://www.researchgate.net/publication/41387164_Associating_Audio-Visual_Activity_Cues_in_a_Dominance_Estimation_Framework
Computationally Efficient Clustering of Audio-Visual Meeting Data. Chapter. Full-text available. ... On conducting experiments on almost 5 hours of audio-visual meeting data, our …
CiteSeerX — Citation Query Overlapped speech detection …
https://citeseerx.ist.psu.edu/showciting?cid=7605534&start=10
This paper investigates the usefulness of segmental phonemedynamics for classification of speaking styles. We modeled transition details based on the phoneme sequences emitted by a speech recognizer, using data obtained from a recording of 39 depressed patients with 7 different speaking styles- normal, pressured, slurred, stuttered, flat, slow and fast speech.
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