## 1 Introduction
The first was aimed at the evaluation of sentences generated by the TTS system using two methods of prosody manipulation--a rule-based method and a modification reflecting the final syllable status [11]. The second compared the differences between the tested sentences produced by the TTS system using three different synthesis methods (standard and deep learning [12; 13]) in combination with rule-based prosody generation. In the first of these experiments, only the corpus-based unit selection (USEL) speech synthesis method [14; 15] was evaluated. Different approaches to prosody modification bring about differences in time duration, phrasing, and time structuring within the synthetic sentences analyzed. Therefore, special types of speech features must be used to enable the detection of these differences in utterance speed, phrase creation, and prosody production by changes in the time domain instead of the standard spectral features. These special supra-segmental features were derived from time durations of voiced and unvoiced parts and were included in the feature set used in this first automatic evaluation experiments. The objective evaluation results of the first experiment were compared with the subjective ratings of human evaluators using the standard listening test. In the second basic evaluation experiment, the three tested types of speech synthesis were the following: (1) the basic USEL synthesis, (2) the synthesis using a deep neural network (DNN) with a long short-term memory (LSTM) and a conventional WORLD vocoder [16], (3) the synthesis using a recurrent neural network with the LSTM and a WaveRNN [17] vocoder. The speech synthesized by the methods using the neural networks is typologically different from that produced by the USEL synthesizer. The USEL artifacts can be found mainly at the points of concatenation of speech units [18], while the neural network synthesis is characterized by problems manifesting perceptually as a certain type of acoustic noise. Thus, the automatic evaluation system developed must satisfy the requirements for the comparison of speech synthesis approaches with essentially different acoustic realizations. In this experiment, the objective results were compared with the subjective ones based on the subjective assessment called MUltiple Stimuli with HiddenReference and Anchor (MUSHRA) listening test [19] for the comparison of speech stimuli using hidden original speech, as well as anchors with different impairments. An auxiliary analysis was carried out to reveal a possible influence of the number of mixture components, the number of synthetic sentences tested, the types of speech features, the types of audio databases for GMM creation, and the dispersion of positions of original utterances in the P-A space on the partial results of the continual GMM P-A classification, as well as on the stability and the accuracy of the final evaluation results. In addition, the influence of the number of mixtures used for GMM creation and training together with 2D classification in the P-A space on the computational complexity (CPU processing time) was investigated. The experiments realized confirm the suitability of the method for this type of task as well as the principal functionality of the system developed.