Machine Learning for Multimodal Interaction, First International Workshop,MLMI 2004, Martigny, Switzerland, June 21-23, 2004, Revised Selected Papers
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.