Is there a place for e-learning in clinical skills? A survey of undergradute medical students experiences and attitudes

e-learning is established in many medical schools. However the effectiveness of e-learning has been difficult to quantify and there have been concerns that such educational activities may be driven more by novelty, than pedagogical evidence. Where some domains may lend themselves well to e-learning, clinical skills has been considered a challenging area for online learning.

Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.