Machine Learning for Multimodal Interaction (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
362
Utgivningsdatum
2005-01-01
Upplaga
2005 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Bengio, Samy (ed.), Bourlard, Hervé (ed.)
Illustrationer
XII, 362 p.
Dimensioner
226 x 152 x 20 mm
Vikt
499 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540245094
Machine Learning for Multimodal Interaction (häftad)

Machine Learning for Multimodal Interaction

First International Workshop, MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers

Häftad Engelska, 2005-01-01
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This book contains a selection of refereed papers presented at the 1st Wo- shop on Machine Learning for Multimodal Interaction (MLMI 2004), held at the "Centre du Parc," Martigny, Switzerland, during June 21-23, 2004. The workshop was organized and sponsored jointly by three European projects, - AMI, Augmented Multiparty Interaction, http://www.amiproject.org - PASCAL, Pattern Analysis, Statistical Modeling and Computational Learning, http://www.pascal-network.org - M4, Multi-modal Meeting Manager, http://www.m4project.org as well as the Swiss National Centre of Competence in Research (NCCR): - IM2: Interactive Multimodal Information Management, http://www.im2.ch MLMI 2004 was thus sponsored by the European Commission and the Swiss National Science Foundation. Given the multiple links between the above projects and several related - search areas, it was decided to organize a joint workshop bringing together - searchers from the di?erent communities working around the common theme of advanced machine learning algorithms for processing and structuring mul- modal human interaction in meetings. The motivation for creating such a forum, which could be perceived as a number of papers from di?erent research dis- plines, evolved from a real need that arose from these projects and the strong motivation of their partners for such a multidisciplinary workshop. This asse- ment was indeed con?rmed by the success of this ?rst MLMI workshop, which attracted more than 200 participants.
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Innehållsförteckning

MLMI 2004.- Accessing Multimodal Meeting Data: Systems, Problems and Possibilities.- Browsing Recorded Meetings with Ferret.- Meeting Modelling in the Context of Multimodal Research.- Artificial Companions.- Zakim - A Multimodal Software System for Large-Scale Teleconferencing.- Towards Computer Understanding of Human Interactions.- Multistream Dynamic Bayesian Network for Meeting Segmentation.- Using Static Documents as Structured and Thematic Interfaces to Multimedia Meeting Archives.- An Integrated Framework for the Management of Video Collection.- The NITE XML Toolkit Meets the ICSI Meeting Corpus: Import, Annotation, and Browsing.- S-SEER: Selective Perception in a Multimodal Office Activity Recognition System.- Mapping from Speech to Images Using Continuous State Space Models.- An Online Algorithm for Hierarchical Phoneme Classification.- Towards Predicting Optimal Fusion Candidates: A Case Study on Biometric Authentication Tasks.- Mixture of SVMs for Face Class Modeling.- AV16.3: An Audio-Visual Corpus for Speaker Localization and Tracking.- The 2004 ICSI-SRI-UW Meeting Recognition System.- On the Adequacy of Baseform Pronunciations and Pronunciation Variants.- Tandem Connectionist Feature Extraction for Conversational Speech Recognition.- Long-Term Temporal Features for Conversational Speech Recognition.- Speaker Indexing in Audio Archives Using Gaussian Mixture Scoring Simulation.- Speech Transcription and Spoken Document Retrieval in Finnish.- A Mixed-Lingual Phonological Component Which Drives the Statistical Prosody Control of a Polyglot TTS Synthesis System.- Shallow Dialogue Processing Using Machine Learning Algorithms (or Not).- ARCHIVUS: A System for Accessing the Content of Recorded Multimodal Meetings.- Piecing Together the Emotion Jigsaw.- Emotion Analysis in Man-Machine Interaction Systems.- A Hierarchical System for Recognition, Tracking and Pose Estimation.- Automatic Pedestrian Tracking Using Discrete Choice Models and Image Correlation Techniques.- A Shape Based, Viewpoint Invariant Local Descriptor.