A new VR application for voice and speech training has emerged from a problem observable in everyday life: an anxiety of public speaking. In the design process, we incorporated both domain knowledge of experts as well as research with end-users in order to explore the needs and the context of the problem. Functionalities of the prototype are the effect of user-centered process in order to suit best their needs and the way they interact with VR environment.

Room reverberation caused by multipath sound wave propagation in acoustic enclosures constitutes an unwanted distortion for automatic speech recognition systems. Multichannel speech enhancement methods often aim to enhance the signal impinging at the microphone array from the source direction while reducing late reverberation. In this paper, we investigate the applicability of spatial filters which constructively combine the direct-path signal with distinct early room reflection signals to increase the direct-to-reverberation ratio and to reduce the word error rate (WER) of automatic speech recognition systems. We present suitable filters and compare them with existing approaches. Results for the simulated acoustic environments indicate that an improvement in WER can indeed be achieved by the spatial filters which account for strong early reflections.

A computationally efficient feature, called Minimum Energy Density (MED) was applied to discriminate audio signals between speech and music in the radio stations programs. The presented binary classifier is based on testing two features: energy distribution and differences between energy in channels. We analyzed 240 hours of signals, from 10 Polish radio stations. Our analysis enables us to provide information about content of particular radio stations.