## 1 Introduction  
Magnetic resonance imaging (MRI) is used to visualize anatomical structures in various medical applications. Apart from whole-body MRI, open-air and extremity MRI also have wide usage. Every MRI scanner contains a gradient coil system generating three orthogonal magnetic fields to scan the object in three spatial dimensions. All these devices produce significant mechanical pulses during the execution of a scan sequence resulting from rapid switching of electrical currents that accompany rapid change in the of direction of the Lorentz force. This mechanical vibration is the source of the acoustic noise radiating from the whole system with possible negative effect on the patients as well as the health personnel [1] manifesting as a stress during or after MRI scanning. MRI is also used to obtain vocal tract shapes during the articulation of speech sounds for the articulatory synthesis [2]. An open-air MRI scanner can be used for this purpose where the examined articulating person lies directly on the plastic cover of the bottom gradient coil while a chosen MR sequence is run. Here the stress-evoking vocal cord tension has an influence on the recorded speech signal [3] by modifying its suprasegmental and spectral features, so it can bring about errors and inaccuracy in the calculation of 3D models of the human vocal tract [4]. This physiological and mental stress can effectively be identified by the parameters derived from the photo-plethysmography (PPG) signal, as heart rate (HR), Oliva-Roztcoil index (ORI) [5] pulse transit time [6], pulse wave velocity [7], blood oxygen saturation, cardiac output [8], and others. The amplitude of the picked-upPPG signal is usually not constant, and it can often be partially disturbed or degraded [9]. The stress is associated with the autonomic nervous system and it can be expressed by higher variability in interbeat intervals (IBI) assessed from the PPG wave as pulse rate variability (PRV) and from the electrocardiogram (ECG) as HR variability (HRV). The variety of frequency spectra determined from PPG and ECG signals can be used for more precise determination of changes in the PRV and HRV values. They are in principle not equivalent because they are caused by different physiological mechanisms. In addition, the level of agreement between the PRV and HRV statistical results depends on several technical factors, e.g., the used sampling frequency or the method of IBI determination [10]. In many people, exposure to acoustic noise and/or vibration causes negative psychological reaction that can be identified with negative emotional states of anger, fear, or panic. Recognition of these negative affective states in the speech signal of the noise-exposed speaker may be used as another stress indicator. All discrete emotions including the six basic ones (anger, disgust, fear, sadness, surprise, joy) can be quantified by two parameters representing dimensions of valence (pleasure) and arousal [11]. The valence dimension reflects changes of the affect from positive (e.g., surprise, joy) to negative (e.g., anger, fear); the arousal dimension ranges from passive (e.g., sadness) to active (e.g., joy, anger) [12]. For emotion detection in the speech signal, various approaches have been used so far. Hidden Markov models were used for performance evaluation of different features: log frequency power coefficients, linear prediction cepstral coefficients, and standard mel-frequency cepstral coefficients (MFCC) [13]. The support vector machines (SVM) [14] employed features extracted from cross-correlograms of emotional speech signals [15]. Another group of speech emotion recognition methods uses artificial neural networks [16]. Recently, machine learning and deep learning approaches have been utilized in this context [17; 18]. However, the technique using Gaussian mixture models (GMM) [19] remains the method of choice when dealing with speech emotion recognition [20; 21].