Investigating the robustness of a Hungarian medical dictation system under various conditions

This paper examines the susceptibility of a dictation system to various types of mismatches between the training and testing conditions. With these experiments we intend to find the best training configuration for the system and also to evaluate the efficiency of the speaker adaptation algorithm we use. The paper first presents the components of the dictation system, and then describes a set of training and recognition experiments where we vary the microphones and create gender-dependent and speakerdependent models. In each case we examine how much the recognition performance can be improved further by speaker adaptation. We conclude that the best and most reliable scores can be obtained by using gender-dependent phone models in combination with speaker adaptation. Speaker adaptation results in great improvements in almost every case. However, our results do not confirm the assumption that the use of one microphone is better than the use of several.