### Related Works  
In recent years, several advanced methodologies and technologies, such as natural language processing, speech recognition, and machine learning, have been investigated for Alzheimer's disease (AD) detection through speech analysis. Different studies have been published analysing acoustic (prosody, voice quality, and pauses), lexical, and semantic features to identify biometric markers of AD using machine learning models, such as random forest [15], linear regression, deep neural networks [16], and ensemble methods, to classify AD status and predict scores on cognitive tests such as the MMSE. In addition, certain results have demonstrated significant associations with hippocampal volume and \(\beta\)-amyloid levels in cerebrospinal fluid, suggesting that these biomarkers may identify cognitive impairment in the preclinical and prodormal stages of AD and predict its progression [17]. These studies report high levels of accuracy in classifying AD and predicting cognitive impairment, with accuracies ranging from 80% to 90%, validating their models on public datasets, such as ADDReSS or AcceXible, as well as local datasets. Moreover, the approaches vary, ranging from the detection of pauses [18] in speech to the analysis of paralinguistic features and verbal fluency [19]. The results of the models are compared with benchmark algorithms, demonstrating significant improvements in accuracy. The research spans multiple languages and testing contexts, suggesting that speech analysis is a promising tool in the early detection of Alzheimer's disease, offering effective indicators of cognitive impairment and validation in diverse linguistic and cultural settings. For its part, automatic emotion recognition from speech is of particular interest because AD patients exhibit changes in how they express their emotions compared to cognitively healthy subjects. Currently, there is no consensus on the number of emotions to be analysed [20]. Most research focuses on the following four basic emotions: anger, fear, sadness, and happiness [21]. In some studies, other emotions, such as surprise and disgust, have also been included [22]. Other research has focused on the development of real-time applications and emphasises the usefulness of representing emotions on an evaluation plane in terms of two or more continuous levels or dimensions [23]. In practice, the most commonly used dimensions to represent emotions are activation and valence levels [24]. The activation level relates to the perceived intensity of the emotion, and the valence level relates to the perceived pleasantness of a stimulus [24]. Numerous studies focusing on automatic emotion recognition from speech have carried out feature extraction based on prosodic aspects related to grammatical structure and lexical stress, such as phonation duration [25; 26; 27], pitch and energy contour, or Teager energy operator [28], related to grammar and lexical stress. Regarding paralinguistic aspects, some studies focus on other types of features, such as the first formant [26; 27] or the energy concentration in different energy bands [29]. Localised studies about emotional speech analysis applied to AD are fundamentally based on three families of parameters:acoustic features such as pitch (standard deviation (SD); maxima and minima) or intensity-related features (SD; maxima and minima), among others; voice-quality features, such as shimmer, local jitter, harmonic-to-noise ratio (HNR), noise-to-harmonic ratio (NHR), and autocorrelation, among others; and duration features such as voice and voiceless fragments. The short-term energy is the main feature analysed. In recent years, a variety of research has explored the use of speech analysis and emotion recognition as noninvasive methods for the early detection of neurocognitive disorders such as Alzheimer's disease.