## 5 Conclusions  
The task of the synthetic speech quality determination by objective measures has been successfully fulfilled by the designed automatic system with continual evaluation on the 2D P-A scale and practical verification on two corpora of the synthetic speech generated by the Czech TTS system. We have theoretical knowledge about a better type of the synthesis (prosody manipulation in the TTS system), but the subjective evaluation performed can show a different opinion of listeners, even though the results of the objective evaluation by this proposed system are generally in correspondence with the theory. The benefit of the proposed method is that the sound/speech material used to create and train the GMMs for P-A classification can be totally unrelated to the synthetic speech tested. The sentences from the original speaker also need not be syntactically or semantically related to the sentences of the TTS system evaluated. The currently developed automatic evaluation system uses a statistical approach and its kernel is practically based on the GMM classifier. The GMM can describe a distribution of given data using a simple k-means method for data clustering implemented in the Netlab toolbox [30]. We automatically expect that all components have Gaussian distributions but their linear combination can approximate non-Gaussian probability distributions for each of the processed P-A classes. In addition, we use a fixed number of mixtures for GMMs without discrimination between the Pleasure/Arousal types of classes and the gender of a speaker (male/female). At present, we are not able to confirm assumption about real distribution of the processed data, so statistical parameters of the training data represented by values in the feature vectors must be investigated in detail. The newer, more complex and more precise method based on spectral clustering [26] can solve this potential problem, so we will try to implement this approach into the GMM creation and training algorithm. Last, but not least, we would like to test adaptive setting of the training procedure (\(N_{\text{MIX}}\), \(N_{\text{ITER}}\), and \(N_{\text{FEAT}}\) parameters) depending on the currently used training data reflecting also the language characteristics (differences in time-duration as well as prosodic parameters). The limitation of the present work lies in the fact that the size of both synthetic speech databases evaluated was relatively small and more sentences must be tested to evaluate the real performance of the proposed automatic system. The second problem is the practical impossibility of direct comparison of our final results with the other subjective evaluation approaches due to incompatible expression of results (in the case of the MUSHRA test) or absence of percentage values (for comparison with the listening test in the form of a confusion matrix). The output of our automatic evaluation system in the form of FEO decisions representing symbolical distances in the 2D P-A space between originals (from a speaker) with the added aspect of subjective emotional meaning by the location in four emotional quadrants. Next, the parameters \(D\)so\({}_{1-2,2,3-3}\) determining differences between the first and the second rank and the second and the third rank in the order are expressed in percentage but, due to the application of emotion quadrant weights, they can reach up to 200%. From the practical point of view, it would be useful to provide an evaluation of the overall computational complexity of the method used in our evaluation process, together with its real-time capabilities, as well as the performance testing of the whole automatic evaluation system. The current realization in the Matlab environment is not very suitable for the building of the application running under Windows or others platforms. If the critical points were found, the whole evaluation algorithm would be implemented in one of the higher programming languages such as C++, C#, Java, etc.