Computationally scalable speaker recognition systems are highly desirable in practice. To achieve this objective, we use a two-stage architecture for text-prompted speaker recognition.

Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36 % reduction of misclassifications on the training data and 57 % on the test data.

Speaker verification is the process of determining the validity of a claimed identity through voice. Traditional approaches to this problem are Gaussian mixture models and hidden Markov models. Although these methods work well, they are difficult to employ in an adaptive framework because of the iterative nature of training. Ideally, as we acquire new-labeled input, we would like to update the verification model immediately to avoid storing speech data (for small memory situations) and to adapt to speaker variability. In this paper, we propose a novel method for adaptive training of polynomial networks. We show that the new method is computationally efficient, requires little memory, and is competitive with batch-based training.