Comprehensive Study of Automatic Speech Emotion Recognition Systems

Speech emotion recognition (SER) is the technology that recognizes psychological characteristics and feelings from the speech signals through techniques and methodologies. SER is challenging because of more considerable variations in different languages arousal and valence levels. Various technical developments in artificial intelligence and signal processing methods have encouraged and made it possible to interpret emotions.SER plays a vital role in remote communication. This paper offers a recent survey of SER using machine learning (ML) and deep learning (DL)-based techniques. It focuses on the various feature representation and classification techniques used for SER. Further, it describes details about databases and evaluation metrics used for speech emotion recognition

An ongoing review of speech emotion recognition

User emotional status recognition is becoming a key feature in advanced Human Computer Interfaces (HCI). A key source of emotional information is the spoken expression, which may be part of the interaction between the human and the machine. Speech emotion recognition (SER) is a very active area of research that involves the application of current machine learning and neural networks tools. This ongoing review covers recent and classical approaches to SER reported in the literature.This work has been carried out with the support of project PID2020-116346GB-I00 funded by the Spanish MICIN

Patients’ verbal emotional expressions and their connection with the psychotherapeutic change: a multi-level analysis of the psychotherapeutic activity

Emotional expressions contribute to the activation and regulation of personal emotional experiences, and communicate something about internal states and intentions. These emotional expressions can be observed in the words we use in our speech. The growing interest in knowing what happens during the psychotherapeutic process has made researchers focus on the study of verbal patient-therapist interaction, considering a notion of performative language, in which language is not only understood as a simple reflection of reality, but as constitutive of it. The present Doctoral Thesis aims at studying the link between verbal emotional expressions and psychotherapeutic change, through the use of five levels of analysis of the psychotherapy: therapy, session, episode, speaking turn and word. The specific objectives that oriented this research were: (a) to determine the differential characteristics of the verbal emotional expression of patients and therapists during Change Episodes; (b) to determine the behavior of these verbal emotional expressions in Change and Stuck Episodes; (c) to determine the behavior of these verbal emotional expressions in each phase of the therapy and throughout the psychotherapeutic process; and (d) to determine which cognitive mechanisms are present in verbal emotional expressions during Change Episodes and throughout the psychotherapeutic process. A mixed methodology was used to analyze 38 Change Episodes (1016 speaking turns) and 19 Stuck Episodes (581 speaking turns) which were identified within two psychodynamic psychotherapeutic processes. Verbal expressions were analyzed using the Therapeutic Activity Coding System (TACS-1.0) which was built to respond to the need to conceptualize and study the verbal activity of patients and therapists. The present Doctoral Thesis is a dossier made up by seven articles that detail the results of each of the aforementioned studies, in order to: (a) identify the main characteristics of the therapeutic conversation during Change Episodes; (b) establish the existence of communicative patterns to work on emotional contents during Change Episodes; (c) determine temporal sequences of interaction between these patterns; (d) analyze the main communicative patterns in order to determine their behavior within Change Episodes, and throughout the different phases of the psychotherapeutic process; and (e) analyze the words verbalized during the use of the main communicative patterns in order to determine the cognitive mechanisms involved in the work of emotional contents during Change Episodes

The Journal of Undergraduate Research: Volume 09

This is the complete issue of the South Dakota State University Journal of Undergraduate Research, Volume 13

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Depression, Volition, and Death: The Effect of Depressive Disorders on the Autonomous Choice to Forgo Medical Treatment

Many contemporary models of medical ethics champion patient autonomy to counterbalance historically paternalistic decision-making processes. These models tend to suggest an autonomous agent free from cognitive bias and systematic distortion (e.g., Kantian or Cartesian rational agents). Evidence is emerging from the fields of cognitive psychology, cognitive science, and neuroscience that fundamentally challenge this cognitive model, demonstrating the dependence of cognition on deeper, avolitional structures (e.g., backstage cognition, cognitive heuristics and biases, automaticity, emotionally-valenced memory, etc.), and hence, shifting the cognitive model towards reductionistic and deterministic philosophies and psychologies. Medical ethics models must adapt their sense of autonomy in light of these findings if the term is to have any meaning - absent this necessary adaptation, medical ethics centers around a cognitive agent that does not actually exist. In contrast to the homuncular models championed (i.e., overly rationalistic and lacking an account of empirically-validated cognitive phenomena), a cognitive model of autonomy is proposed, along with useful psychometrics and a case metric to assist clinicians in assessing the possibility of compromised autonomy in patients electing to forgo medical treatment

The use of Somatic Experiencing™ in the treatment of an adolescent with trauma-based obsessive-compulsive disorder

Abstract: When trauma precedes or coincides with the development of obsessive-compulsive disorder (OCD), a unique type of OCD develops. This is because, as the research shows, the neurophysiological reactions of trauma become “trapped” in the physiology of the client’s nervous system. Therefore, it is proposed that trauma adds an additional dimension to the treatment of OCD in paediatric and adolescent clients. It then becomes necessary to utilize a trauma-based treatment method, such as Somatic Experiencing™, to assist in resolving the obsessions and compulsions. The developmental features of adolescence are of particular interest to the area of educational psychology. It has been established through research cited in this thesis that childhood trauma significantly affects neuronal development and Autonomic Nervous System regulation, and creates a greater predisposition to mental illness in adolescence and adulthood.. The adolescent brain is well known for the increased expression of dopamine in the prelimbic Prefrontal Cortex which is instrumental in increasing motivational salience. Due to this factor, associations formed by the adolescent between a behaviour and the environment are more difficult to change compared with other maturational stages. (Baker, Bisby & Richardson, 2016)...Ph.D. (Educational Psychology

The Persistence of Involuntary Memory: Analyzing Phenomenology, Links to Mental Health, and Content

In daily life, memories of one’s personal past are often retrieved involuntarily (i.e., unintentionally and effortlessly). Termed involuntary autobiographical memories (IAMs), recent evidence suggests that these are often recurrent (i.e., the same event is remembered repetitively), though controversy surrounds their basic nature. Some research suggests that they are mostly positive or benign, whereas others suggest that they directly contribute to mental health disorders. Here, we show that while recurrent IAMs are common and frequent in general populations, they consistently predict symptoms of mental health disorders. In Study 1, we characterized recurrent IAMs in a large-scale survey of undergraduates. Most participants had experienced recurrent IAMs within the past year (52%), most of which were self-rated as negative in valence (52%). Experiencing negative recurrent IAMs predicted significantly more symptoms of depression, posttraumatic stress, social anxiety, and general anxiety. In Studies 2a and 2b, we examined whether age and trait emotion regulation might modulate recurrent IAMs, because older adults are well-known to have enhanced emotion regulation compared to younger adults. Results indicated that age (Study 2a) reversed the valence distribution: younger adults’ recurrent IAMs were mostly negative, whereas older adults’ were mostly positive. Further, trait emotion regulation (Study 2b) also modulated valence in a sample of younger adults: high emotion regulators were significantly less likely to report negative recurrent IAMs. Regardless of age or trait emotion regulation, experiencing negative recurrent IAMs again predicted greater symptoms of mental health disorders. In Study 3, we asked how analyzing content (e.g., written descriptions of recurrent IAMs) might expand our understanding of these memories, beyond self-reported valence ratings. We developed the first adaptation of computational methods (e.g., machine learning) to understand autobiographical memory content, enabling us to discover content categories (“topics”) in recurrent IAMs. We found that participants experienced recurrent IAMs about a variety of events, ranging from the mundane to the extreme. In Study 4, we extended this computational approach to measure how content might predict mental health above and beyond self-reported valence ratings. Results indicated that elevated symptoms of each disorder were uniquely related to recurrent IAMs about specific topics. Our results suggest that it is imprecise to say that negative recurrent IAMs are related to increased symptoms – our current work pinpoints which specific topics in recurrent IAMs predict mental health. This dissertation provides insight into the nature of recurrent IAMs in large samples of general populations. Importantly, this dissertation distinguishes how these memories and their relationships to mental health are modulated by individual differences. Finally, this dissertation provides a novel framework and methodology (e.g., computational text analysis) for analyzing autobiographical memory content in concert with phenomenology, opening avenues for research to be conducted at an unprecedented scope and scale