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.

A “very limited” effect or a “seismic” impact? A study of the impact of the Human Rights Act 1998 on the Courts of Northern Ireland

This article focuses on the question of what impact the Human Rights Act 1998 has had in practice on the courts of Northern Ireland. How frequently are human rights arguments made in the course of cases in this jurisdiction, and to what extent do such arguments affect outcomes of cases? In order to assess the impact of the Act, the use of the European Convention on Human Rights in the Northern Irish courts during four periods of time is examined. These are, firstly, prior to the passing of the Act in November 1998; secondly, between the Act’s passing and its coming into force in October 2000; thirdly, the first three years after the coming into force of the Act (October 2000 until October 2003); and fourthly, the three years between October 2006 and October 2009.

Inter-Frame Contextual Modelling For Visual Speech Recognition

In this paper, we present a new approach to visual speech recognition which improves contextual modelling by combining Inter-Frame Dependent and Hidden Markov Models. This approach captures contextual information in visual speech that may be lost using a Hidden Markov Model alone. We apply contextual modelling to a large speaker independent isolated digit recognition task, and compare our approach to two commonly adopted feature based techniques for incorporating speech dynamics. Results are presented from baseline feature based systems and the combined modelling technique. We illustrate that both of these techniques achieve similar levels of performance when used independently. However significant improvements in performance can be achieved through a combination of the two. In particular we report an improvement in excess of 17% relative Word Error Rate in comparison to our best baseline system.