The most popular systems for automatic sign language recognition are based on vision. They are user-friendly, but very sensitive to changes in regard to recording conditions. This paper presents a description of the construction of a more robust system-an accelerometer glove-as well as its application in the recognition of sign language gestures. The basic data regarding inertial motion sensors and the design of the gesture acquisition system as well as project proposals are presented. The evaluation of the solution presents the results of the gesture recognition attempt by using a selected set of sign language gestures with a described method based on Hidden Markov Model (HMM) and parallel HMM approaches. The proposed usage of parallel HMM for sensor-fusion modeling reduced the equal error rate by more than 60%, while preserving 99.75% recognition accuracy.

The paper presents the concept of the embedded solution for voice biometric access system. The most important requirements for access control systems are presented, as well as the resulting design intent. The architecture of the system, its functionality and the methods used to verify the speakers is described along with a discussion of performance optimization methods of the implementation. Issues of voice biometrics and automatic speech processing are discussed as well.

A new event-driven method of speech signals segmentation is presented. The wavelet discrete transform was used for spectral analysis and to create a segmentation procedure. Innovative event detector is the core of the process. Efficiency of the algorithm is tested against the hand annotated speech corpus.