Multimodal Meeting Tracker

Abstract

Face-to-face meetings usually encompass several modalities including speech, gesture, handwriting, and person identification. Recognition and integration of each of these modalities is important to create an accurate record of a meeting. However, each of these modalities presents recognition difficulties. Speech recognition must be speaker and domain independent, have low word error rates, and be close to real time to be useful. Gesture and handwriting recognition must be writer independent and support a wide variety of writing styles. Person identification has difficulty with segmentation in a crowded room. Furthermore, in order to produce the record automatically, we have to solve the assignment problem (who is saying what), which involves people identification and speech recognition. We follow a multimodal approach for people identification to increase the robustness (with the modules: color appearance id, face id and speaker id). This paper will examine a meeting room system under development at Carnegie Mellon University that enables us to track, capture and integrate the important aspects of a meeting from people identification to meeting transcription. Once a multimedia meeting record is created, it can be archived for later retrieval. This paper will review our meeting browser that we have developed which facilitates tracking and reviewing meetings.