An overview of the OASIS speech recognition project

This paper presents an overview of the "OASIS" segment-based speech recognition system developed at the Research Group on Artificial Intelligence of the Hungarian Academy of Sciences. We present the preprocessing method, the features extracted from its output, and how segmentation of the input signal is done based on those features. We also describe the two types of evaluation functions we applied for phoneme recognition, namely a C4.5 and an instance-based learning technique. In our system, the recognition of words from a vocabulary means a special search in a hypothesis space; we present how this search space is handled and the search is performed. Our results demonstrate that for small vocabularies we obtained acceptable recognition rates of about 90% even with the very few features and small training database used. It is now a matter of further investigation to see how much these methods could be extended to be applicable to large vocabulary speech recognition.