###### Abstract
Other techniques such as Automatic Spontaneous Speech Analysis (ASSA) [19] are presented as methods that encompass different combined voice qualities (durations, short time energy and spectral centroid) and provide relevant information by classifying these data, in most cases using Support Vector Machine, k-Nearest Neighbors, Linear Discriminant Analysis [20] or Multi-Layer Perceptron [21] classifiers. Currently, studies also include classical characteristics in emotional analysis such as pitch, intensity and variation of frequency components and, more recently, ET [22]. They present methods like ASSA and Emotional Speech Analysis (ESA) that, combined with ET and Spontaneous Speech (SS) tasks, have achieved discrimination between patients with AD and healthy controls with 94% accuracy using an SVM classifier [23]. Numerous studies have also been published on the basis of Voice Activity Detector (VAD) transcriptions [24] which, in addition to an acoustic analysis of the voice or speech signal, offer lexical, semantic, punctuation or syntactic analysis of the communicative process [25; 26]. Since approximately 2012, more and more works in this line point to the need to seek support in the non-linear and non-stationary aspects [15]. Some researchers have proposed that subtle cognitive changes in the early and preclinical stages could be more accurately detected by fractals. In some works they are presented in combination with other non-conventional measures such as the Hurst exponent [27] or just in combination with linear characteristics [28]. It is also important to outline that, so far, one of the main limitations has been the scarcity and, in turn, diversity of samples available to train models for the evolutionary control of AD. Although there are databases of the most varied disciplines for the study of AD, ranging from meta-analyzes focused on the genetic association with putative Alzheimer's disease [29] to others based on the olfactory system for the precocious in Parkinson's and Alzheimer's [30], the reality is that the most of the located databases regarding the speech recognition in AD lack the required amount of data to carry out a truly consistent analysis and there is also the disadvantage of having been established according to different guidelines and criteria. Most localized databases focus on spontaneous speech recordings [31; 32; 33], although there are others that invite the subject to perform tasks as repetition or reading [20]. The recording of the samples is usually carried out in a timely manner on a number of control subjects and AD patients classified according to different degrees of severity: mild, moderate and severe [34; 35; 36; 37]. Although less, some have carried out longitudinal studies on the subjects [38; 39] in order to find a relationship between the progression of AD and language impairment. As a review of the state of the art, localized databases that include recordings for linguistic analysis of AD patients are listed in Table 1. Information is also included on the type of interviewer used in them; human/automatic, and on the type of study over time: longitudinal/transverse. This table includes other information such as the language used by the subjects in the recordings, the classification of the participants (Healthy Control (HC), Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD)) and the linguistic tasks performed. It is important to clarify how we have classified the tasks used for the recordings of each of the studies. In this way, we define: spontaneous speech (SS), which includes telling stories, experiences, answering questions freely or describing images, inter alia. Reading, repetition, counting or animal denomination tasks (cVF) are others that appear in some studies, although less, and we understand that they are not related to the concept of spontaneous speech, as we define it. We have used the term Mixed when both SS and other tasks have been included in the study.