The objective of the research presented in this thesis was to systematically investigate the computational structure for cross-database emotion recognition. The research consisted of evaluating the stability of acoustic features, particularly the glottal and Teager Energy based features, and investigating three normalization methods and two data fusion techniques. One of the challenges of cross-database training and testing is accounting for the potential variation in the types of emotions expressed as well as the recording conditions. In an attempt to alleviate the impact of these types of variations, three normalization methods on the acoustic data were studied. Motivated by the lack of large and diverse enough emotional database to train the classifier, using multiple databases to train posed another challenge: data fusion. This thesis proposed two data fusion techniques, pre-classification SDS and post-classification ROVER to study the issue. Using the glottal, TEO and TECC features, of which the stability of emotion distinguishing ability has been highlighted on multiple databases, the systematic computational structure proposed in this thesis could improve the performance of cross-database binary-emotion recognition by up to 23% for neutral vs. emotional and 10% for positive vs. negative.