Optimal weighting of bimodal biometric information with specific application to audio-visual person identification

A new method is proposed to estimate the optimal weighting parameter for combining audio (speech) and visual (face) information in person identification, based on estimating probability density functions (pdfs) for classifier scores under Gaussian assumptions. Performance comparisons with real and simulated data indicate that this method has advantages in reducing bias and variance of the estimation relative to other methods tried, so achieving a robust estimator of the optimal weighting parameter. Another contribution is that we propose the bootstrap method to compare performances of different algorithms for estimating the optimal weighting parameter, so providing a strict criterion in comparing algorithms of this kind. Using simulated data, for which the pdf is controlled and known, we show that the advantages of the method hold up when the underlying Gaussian assumption is violated. The main drawback is that we have to choose an adjustable parameter, and it is not clear how this should best be done

Arabic Text to Arabic Sign Language Translation System for the Deaf and Hearing-Impaired Community

This paper describes a machine translation system that offers many deaf and hearing impaired people the chance to access published information in Arabic by translating text into their first language, Arabic Sign Language (ArSL). The system was created under the close guidance of a team that included three deaf native signers and one ArSL interpreter. We discuss problems inherent in the design and development of such translation systems and review previous ArSL machine translation systems, which all too often demonstrate a lack of collaboration between engineers and the deaf community. We describe and explain in detail both the adapted translation approach chosen for the proposed system and the ArSL corpus that we collected for this purpose. The corpus has 203 signed sentences (with 710 distinct signs) with content restricted to the domain of instructional language as typically used in deaf education. Evaluation shows that the system produces translated sign sentences outputs with an average word error rate of 46.7% and an average position error rate of 29.4% using leave-one out cross validation. The most frequent source of errors is missing signs in the corpus; this could be addressed in future by collecting more corpus material