## 3 Experiments  
### Material Used, Initial Settings, and Conditions  
To evaluate synthetic speech quality by continual classification in the P-A scale, we collected the first speech corpus (SC1) consisting of three parts: the original speech uttered by real speakers, and two variations of speech synthesis produced by the Czech TTS system using the USEL method [16] with voices based on the original speaker. Two methods of prosody manipulation were applied: the rule-based method (assigned as \(TTS_{A}\)) and the modified version reflecting the final syllable status (as \(TTS_{B}\)) [11]. The natural as well as the synthetic speech originates from four professional speakers--two males (M1, M2) and two females (F1, F2). Declarative sentences were used for each of four original speakers (50 + 50/50 + 50; it means 200 in total). As regards the synthesis, we used 2 \(\times\) 50/40 (for M1/M2) and 2 \(\times\) 40/40 (for F1/F2) sentences of two synthesis types from each of the four voices--340 in total for all the voices. The speech signals were sampled at 16 kHz and their duration ranged from 2.5 to 5 s.  
The second collected speech corpus (SC2) consists of four parts: the natural speech uttered by the original speakers and three variations of speech synthesis: the USEL based TTS system (assigned to \(Synt1\)) and two LSTM based systems with different vocoders: conventional WORLD (further referred to as \(Synt2\)) [16], WaveRNN (referred to as \(Synt3\)) [17]. As in the case of SC1, the original and synthetic speech originated from the speakers M1, M2, and F1, F2. This means, that 200 original sentences and 600 synthetic ones (200 for each of the synthesis types) were used in this work. The processed synthetic speech signals with the duration from 2 to 12 s were resampled at 16 kHz. The detailed description of the speech material used is provided in Table 1.  
To create and train the GMM models of the Pleasure/Arousal classes, two separate databases were used. The first was the International Affective Digitized Sounds (IADS-2) [28] database (further referred to as DB1). It consists of 167 sound and noise records produced by humans, animals, simple instruments, the industrial environment, weather, music, etc. Every sound was repeatedly evaluated by listeners, so the database contains the mean values of Pleasure and Arousal parameters within the range of \(<\)l \(\sim\) 9\(\cdot\). All the records of sounds used with the duration of 6 s were resampled at 16 kHz to comply with the tested as well as original speech signals. In this case, the GMM models are common for male and female voices. The second database used was the MSP-IMPROV audiovisual database [29] in the English language (further referred to as DB2). From this database, we used only declarative sentences in four emotional states (angry, sad, neutral, and happy) uttered by three male and three female speakers. Finally, 2 \(\times\) 240 sentences (separately for male and female voices) with duration from 0.5 to 6.5 s were used. For compatibility with the DB1, all of the applied speech signals were resampled at 16 kHz and the mean P-A values were recalculated to fit the range \(<\)l \(\sim\) 9\(\cdot\) of the DB1. These two databases were used because they contain all the records with evaluation results on the P-A scale and were freely accessible without any fee or other restrictions. The speech/sound signal analyzed is processed by a pitch-asynchronous method per frame with one half overlapping. The frame length of 24/20 ms was used for male/female voices according to F0 values of the current speaker--see the second column in Table 1. For the calculation of spectral and cepstral properties, the number of fast Fourier transform