## 1 Introduction  
At present, many different subjective and objective methods and criteria for quality evaluation of synthetic speech produced by text-to-speech (TTS) systems are used. For the subjective assessment of synthesis quality, listening tests are generally acknowledged. The conventional listening tests usually involve a comparison category rating on a scale from \"much better\" to \"much worse\" than high-quality reference speech [1]. Perceptual characteristics may be divided into five basic dimensions--(1) naturalness of voice, and its pleasantness, (2) prosodic quality including accentuation, rhythm, and intonation, (3) fluency and intelligibility, (4) absence of disturbances, (5) calmness--with the first three being the best for capturing the integral quality [2]. Apart from the naturalness and understandability of contents, listening tests can also measure the distinguishability of characters or the degree of entertainment [3]. The subjective scales for rating the synthesized speech may include only a few scored parameters, such as an overall impression by a mean opinion score (MOS) describing the perceived speech quality from poor to excellent, a valence from negative to positive, and an arousal from unexcited to excited [4]. The MOS scale can be used not only for naturalness, but for different dimensions, such as affect (from negative to positive) or speaking style (from irritated to calm) as well [5]. The comparison of a pair of utterances synthesized by different methods or originating from different speech inventories is often carried out by a preference listening test [6]. For objective speech quality estimation of the TTS voice, various speech features extracted from the natural and synthetic speech are evaluated. In [7] the mel frequency cepstral coefficients (MFCC) and the modified group delay function were used as a dynamic time warping (DTW)-based fusion of magnitude and phase features. The DTW alignment of reference and synthesized spectral sequences was also carried out in combination with the average spectral distortion [8]. In addition to the MFCC distance, pitch frequency (F0) related features can be used to compare a reference natural signal with a copy-synthesis: voicing accuracy, a gross pitch error, and a fine pitch error [9]. The synthetic speech quality may be predicted by a mix of several prosodic properties (slope of F0, F0 range, jitter, shimmer, vocalic durations, intervocalic durations) and articulation-associated properties (discrete-cosine-transform coefficients of the mel-cepstrum, their delta, and delta-delta values) [2]. Our current research focuses on the development of an automatic system for the quality evaluation of synthetic speech in the Czech language using different synthesis methods. It was motivated by our assumption of the successful application of a 2D emotional model with a Pleasure-Arousal (P-A) scale [10] for automatic evaluation of synthetic speech quality based on the Gaussian mixture model (GMM) classification. In such a manner, the subjectivity of human assessment and considerable time consumption during the standard listening tests can be eliminated. The proposed system is based on the principle of determination of similarities/differences between the original sentences uttered by a speaker and the sentences synthesized using the speech material of the same speaker. The final evaluation result based on Euclidean distances in the P-A space expresses the order of synthesis proximity between different speech syntheses and the original speech. The audio material used for the GMM creation and training originated from the sound/speech databases that were directly labeled in the P-A scale so that the subsequent GMM classification process yielded a combination of Pleasure and Arousal classes corresponding to the speech stimuli tested. Within the framework of the work presented, two basic evaluation experiments with the Czech speech synthesizer of male and female voices were performed.