### Related Works
These studies employ different datasets and methodologies, such as emotional prosody recognition (EPR), the ability to understand emotions through tone of voice [30], or the use of the Hurst exponent [31], to analyse speech signals in different languages, being able to effectively differentiate between emotions of anger and sadness, regardless of the language. Similarly, other studies assess emotions of frustration in picture description using speech emotion recognition (SER) to measure disease progression [32]. Intelligent and noninvasive computational techniques based on emotional feature extraction from speech and pattern recognition using neural networks have also been employed [33]. The results obtained in these studies are remarkable, showing a high sensitivity in distinguishing between healthy and cognitively impaired patients and among different degrees of disease severity. These studies suggest that deficits in emotional recognition are a part of AD symptoms and that speech analysis and emotion recognition offer promising noninvasive tools for the early diagnosis of Alzheimer's disease, potentially improving patients' quality of life through earlier and more accessible detection. These methods are effective regardless of the language, demonstrating their universal applicability and reduction in the costs and time compared to traditional methods. In this context, on the basis of the temporal segmentation of the speech signal, the concept of emotional temperature (ET) has been defined [24]. This is a characteristic related to the emotional charge of speech calculated from various prosodic and paralinguistic aspects. Among the acoustic features of speech, the fundamental frequency of the samples is considered the main prosodic indicator, specifically the intonation given by the pitch contour. Furthermore, the accumulation of acoustic energy in different frequency bands, which varies according to the speech production model, can also be used as a paralinguistic indicator of emotional state. In emotional speech, the energy at higher frequencies increases compared to nonemotional speech. To calculate the ET, two prosodic features (related to pitch) and four paralinguistic features (related to energy) are extracted from each speech signal fragment [24]. From ET and the use of support vector machine (SVM) classifiers, some studies [24] have managed to classify speech samples as pathological and nonpathological, obtaining strong results for AD detection. In the same vein, combined emotional response analysis (ERA) methods have been developed that employ different linear features, such as pitch, intensity, or variation in the frequency components. Along with ET measures and spontaneous speech tasks, they have achieved discrimination between health control (HC) subjects and AD patients with high accuracy rates. Combining ASSA analysis [34; 35] and features such as ET, it has been possible to demonstrate a significant loss of fluency in people with AD regarding the duration and percentage of voiced and unvoiced segments [35; 36], with an accuracy of 92.24%. Combining ASSA, ESA, and ET, results can be achieved using SVMs with an accuracy of around 94% [34]. It is worth noting that ET has also been employed, together with the fractal dimension (FD), in some publications, in the context of the automatic analysis of emotional response (AAER) from spontaneous speech [37]. In that sense, they have demonstrated that the inclusion of fractal dimension features adds relevant information regarding the nonlinearity in speech signals and the appropriate analysis of emotional response [37]. In any case, studies based on ET, exclusively or in combination with other features, have shown promising results regarding the definition of useful features in the early diagnosis of AD.