## 1 Introduction
Much better scores are achieved by a fusion of different recognition methods, e.g., GMM and SVM in speaker age and gender identification [22] or in speaker verification [23], or SVM and K-nearest neighbour in speech emotion recognition [24]. Another improvement may be achieved by multimodal approach to emotion recognition using a fusion of features extracted from audio signals, text transcriptions, and visual signals of face expressions [25]. In this sense, we use two modalities for stress detection in this paper: the recorded speech signal and the sensed PPG signal. Our research aim is to detect and quantify the effect of vibration and acoustic noise during the MR scan examination on vocal cords of an examined person. In the performed experiments, the tested person articulated while lying in the scanning area of the open-air low field MRI tomograph [26]. The levels of the vibration and noise in the MRI depend on several factors [27; 28]. At first, they comprise a class of a scan sequence based on a physical principle of generation of the free induction decay (FID) signal by the non-equilibrium nuclear spin magnetization precession (gradient or spin echo classes). Next, they depend on the used methodology of MR image construction from received FID signals (standard, turbo, hi-resolution, 3D, etc.). Finally, the basic parameters of MR scan sequences (repetition time TR, echo time TE, slice orientation, etc.) and additional settings (number of accumulations, number of slices, their thickness, etc.) are chosen depending on the required final quality of MR images. All these parameters together with an actual volume depending on a tested person's weight have influence on the intensity of the produced vibration and noise, on the time duration of the MR scan process, and finally on the stimulated physiological and psychological stress in the examined persons. In previous research [29; 30] the measured PPG signals together with the derived HR have already been used to monitor the physiological impact of vibration and acoustic noise on a person examined inside the MRI scanning device. This paper describes the current experimental work focused on stress detection and evaluation from speech records of vowel phonation picked up together with PPG signals. The whole experiment consists of four measurement phases representing different stress conditions for the tested person. The PPG and phonation signal measurement of the first and the fourth phases is realized in the laboratory conditions; in the second and third phases the tested person lies inside the MRI equipment; the third measurement phase is realized after exposure to vibration and noise during scanning in the MRI device. The first part of the proposed method for stress detection and evaluation uses the recorded phonation signal. From this signal, different speech features are determined for statistical analysis and evaluation with the help of a GMM classifier. For GMM creation and training, one database of affective sounds and two databases containing emotional speech are used. The second part of the stress evaluation method gives comparison of the results obtained from the statistical processing of HR and ORI values determined from the PPG signal. This is supplemented by comparison of energetic, time, and statistical parameters describing the sensed PPG waves. The fusion of the results obtained from both types of stress analysis methods gives the final stress level.