Automatic vocal recognition of a child's perceived emotional state within the Speechome corpus

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 137-149).With over 230,000 hours of audio/video recordings of a child growing up in the home setting from birth to the age of three, the Human Speechome Project has pioneered a comprehensive, ecologically valid observational dataset that introduces far-reaching new possibilities for the study of child development. By offering In vivo observation of a child's daily life experience at ultra-dense, longitudinal time scales, the Speechome corpus holds great potential for discovering developmental insights that have thus far eluded observation. The work of this thesis aspires to enable the use of the Speechome corpus for empirical study of emotional factors in early child development. To fully harness the benefits of Speechome for this purpose, an automated mechanism must be created to perceive the child's emotional state within this medium. Due to the latent nature of emotion, we sought objective, directly measurable correlates of the child's perceived emotional state within the Speechome corpus, focusing exclusively on acoustic features of the child's vocalizations and surrounding caretaker speech. Using Partial Least Squares regression, we applied these features to build a model that simulates human perceptual heuristics for determining a child's emotional state. We evaluated the perceptual accuracy of models built across child-only, adult-only, and combined feature sets within the overall sampled dataset, as well as controlling for social situations, vocalization behaviors (e.g. crying, laughing, babble), individual caretakers, and developmental age between 9 and 24 months. Child and combined models consistently demonstrated high perceptual accuracy, with overall adjusted R-squared values of 0.54 and 0.58, respectively, and an average of 0.59 and 0.67 per month. Comparative analysis across longitudinal and socio-behavioral contexts yielded several notable developmental and dyadic insights. In the process, we have developed a data mining and analysis methodology for modeling perceived child emotion and quantifying caretaker intersubjectivity that we hope to extend to future datasets across multiple children, as new deployments of the Speechome recording technology are established. Such large-scale comparative studies promise an unprecedented view into the nature of emotional processes in early childhood and potentially enlightening discoveries about autism and other developmental disorders.by Sophia Yuditskaya.S.M

Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.Comment: To appear in Expert Systems with Applications. Accompanying INTERSPEECH 2022 Tutorial on the same topic. Including latest advancements in large language models (LLMs

Classification et Caractérisation de l'Expression Corporelle des Emotions dans des Actions Quotidiennes

The work conducted in this thesis can be summarized into four main steps.Firstly, we proposed a multi-level body movement notation system that allows the description ofexpressive body movement across various body actions. Secondly, we collected a new databaseof emotional body expression in daily actions. This database constitutes a large repository of bodilyexpression of emotions including the expression of 8 emotions in 7 actions, combining video andmotion capture recordings and resulting in more than 8000 sequences of expressive behaviors.Thirdly, we explored the classification of emotions based on our multi-level body movement notationsystem. Random Forest approach is used for this purpose. The advantage of using RandomForest approach in our work is double-fold : 1) reliability of the classification model and 2) possibilityto select a subset of relevant features based on their relevance measures. We also comparedthe automatic classification of emotions with human perception of emotions expressed in differentactions. Finally, we extracted the most relevant features that capture the expressive content of themotion based on the relevance measure of features returned by the Random Forest model. Weused this subset of features to explore the characterization of emotional body expression acrossdifferent actions. A Decision Tree model was used for this purpose.Ce travail de thèse peut être résumé en quatre étapes principales. Premièrement, nousavons proposé un système d’annotation multi-niveaux pour décrire le mouvement corporel expressif dansdifférentes actions. Deuxièmement, nous avons enregistré une base de données de l’expression corporelledes émotions dans des actions quotidiennes. Cette base de données constitue un large corpus de comportementsexpressifs considérant l’expression de 8 émotions dans 7 actions quotidiennes, combinant à la fois lesdonnées audio-visuelle et les données de capture de mouvement et donnant lieu à plus que 8000 séquencesde mouvement expressifs. Troisièmement, nous avons exploré la classification des émotions en se basantsur notre système d’annotation multi-niveaux. L’approche des forêts aléatoires est utilisée pour cette fin. L’utilisationdes forêts aléatoires dans notre travail a un double objectif : 1) la fiabilité du modèle de classification,et 2) la possibilité de sélectionner un sous-ensemble de paramètres pertinents en se basant sur la mesured’importance retournée par le modèle. Nous avons aussi comparé la classification automatique des émotionsavec la perception humaine des émotions exprimées dans différentes actions. Finalement, nous avonsextrait les paramètres les plus pertinents qui retiennent l’expressivité du mouvement en se basant sur la mesured’importance retournée par le modèle des forêts aléatoires. Nous avons utilisé ce sous-ensemble deparamètres pour explorer la caractérisation de l’expression corporelle des émotions dans différentes actionsquotidiennes. Un modèle d’arbre de décision a été utilisé pour cette fin

Improving the Generalizability of Speech Emotion Recognition: Methods for Handling Data and Label Variability

Emotion is an essential component in our interaction with others. It transmits information that helps us interpret the content of what others say. Therefore, detecting emotion from speech is an important step towards enabling machine understanding of human behaviors and intentions. Researchers have demonstrated the potential of emotion recognition in areas such as interactive systems in smart homes and mobile devices, computer games, and computational medical assistants. However, emotion communication is variable: individuals may express emotion in a manner that is uniquely their own; different speech content and environments may shape how emotion is expressed and recorded; individuals may perceive emotional messages differently. Practically, this variability is reflected in both the audio-visual data and the labels used to create speech emotion recognition (SER) systems. SER systems must be robust and generalizable to handle the variability effectively. The focus of this dissertation is on the development of speech emotion recognition systems that handle variability in emotion communications. We break the dissertation into three parts, according to the type of variability we address: (I) in the data, (II) in the labels, and (III) in both the data and the labels. Part I: The first part of this dissertation focuses on handling variability present in data. We approximate variations in environmental properties and expression styles by corpus and gender of the speakers. We find that training on multiple corpora and controlling for the variability in gender and corpus using multi-task learning result in more generalizable models, compared to the traditional single-task models that do not take corpus and gender variability into account. Another source of variability present in the recordings used in SER is the phonetic modulation of acoustics. On the other hand, phonemes also provide information about the emotion expressed in speech content. We discover that we can make more accurate predictions of emotion by explicitly considering both roles of phonemes. Part II: The second part of this dissertation addresses variability present in emotion labels, including the differences between emotion expression and perception, and the variations in emotion perception. We discover that it is beneficial to jointly model both the perception of others and how one perceives one’s own expression, compared to focusing on either one. Further, we show that the variability in emotion perception is a modelable signal and can be captured using probability distributions that describe how groups of evaluators perceive emotional messages. Part III: The last part of this dissertation presents methods that handle variability in both data and labels. We reduce the data variability due to non-emotional factors using deep metric learning and model the variability in emotion perception using soft labels. We propose a family of loss functions and show that by pairing examples that potentially vary in expression styles and lexical content and preserving the real-valued emotional similarity between them, we develop systems that generalize better across datasets and are more robust to over-training. These works demonstrate the importance of considering data and label variability in the creation of robust and generalizable emotion recognition systems. We conclude this dissertation with the following future directions: (1) the development of real-time SER systems; (2) the personalization of general SER systems.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147639/1/didizbq_1.pd

ENHANCING EXPRESSIVITY OF DOCUMENT-CENTERED COLLABORATION WITH MULTIMODAL ANNOTATIONS

As knowledge work moves online, digital documents have become a staple of human collaboration. To communicate beyond the constraints of time and space, remote and asynchronous collaborators create digital annotations over documents, substituting face-to-face meetings with online conversations. However, existing document annotation interfaces depend primarily on text commenting, which is not as expressive or nuanced as in-person communication where interlocutors can speak and gesture over physical documents. To expand the communicative capacity of digital documents, we need to enrich annotation interfaces with face-to-face-like multimodal expressions (e.g., talking and pointing over texts). This thesis makes three major contributions toward multimodal annotation interfaces for enriching collaboration around digital documents. The first contribution is a set of design requirements for multimodal annotations drawn from our user studies and explorative literature surveys. We found that the major challenges were to support lightweight access to recorded voice, to control visual occlusions of graphically rich audio interfaces, and to reduce speech anxiety in voice comment production. Second, to address these challenges, we present RichReview, a novel multimodal annotation system. RichReview is designed to capture natural communicative expressions in face-to-face document descriptions as the combination of multimodal user inputs (e.g., speech, pen-writing, and deictic pen-hovering). To balance the consumption and production of speech comments, the system employs (1) cross-modal indexing interfaces for faster audio navigation, (2) fluid document-annotation layout for reduced visual clutter, and (3) voice synthesis-based speech editing for reduced speech anxiety. The third contribution is a series of evaluations that examines the effectiveness of our design solutions. Results of our lab studies show that RichReview can successfully address the above mentioned interface problems of multimodal annotations. A subsequent series of field deployment studies test the real-world efficacy of RichReview by deploying the system for document-centered conversation activities in classrooms, such as instructor feedback for student assignments and peer discussions about course material. The results suggest that using rich annotation helps students better understand the instructor’s comments, and makes them feel more valued as a person. From the results of the peer-discussion study, we learned that retaining the richness of original speech is the key to the success of speech commenting. What follows is the discussion on the benefits, challenges, and future of multimodal annotation interfaces, and technical innovations required to realize the vision

Gesture and Speech in Interaction - 4th edition (GESPIN 4)

International audienceThe fourth edition of Gesture and Speech in Interaction (GESPIN) was held in Nantes, France. With more than 40 papers, these proceedings show just what a flourishing field of enquiry gesture studies continues to be. The keynote speeches of the conference addressed three different aspects of multimodal interaction:gesture and grammar, gesture acquisition, and gesture and social interaction. In a talk entitled Qualitiesof event construal in speech and gesture: Aspect and tense, Alan Cienki presented an ongoing researchproject on narratives in French, German and Russian, a project that focuses especially on the verbal andgestural expression of grammatical tense and aspect in narratives in the three languages. Jean-MarcColletta's talk, entitled Gesture and Language Development: towards a unified theoretical framework,described the joint acquisition and development of speech and early conventional and representationalgestures. In Grammar, deixis, and multimodality between code-manifestation and code-integration or whyKendon's Continuum should be transformed into a gestural circle, Ellen Fricke proposed a revisitedgrammar of noun phrases that integrates gestures as part of the semiotic and typological codes of individuallanguages. From a pragmatic and cognitive perspective, Judith Holler explored the use ofgaze and hand gestures as means of organizing turns at talk as well as establishing common ground in apresentation entitled On the pragmatics of multi-modal face-to-face communication: Gesture, speech andgaze in the coordination of mental states and social interaction.Among the talks and posters presented at the conference, the vast majority of topics related, quitenaturally, to gesture and speech in interaction - understood both in terms of mapping of units in differentsemiotic modes and of the use of gesture and speech in social interaction. Several presentations explored the effects of impairments(such as diseases or the natural ageing process) on gesture and speech. The communicative relevance ofgesture and speech and audience-design in natural interactions, as well as in more controlled settings liketelevision debates and reports, was another topic addressed during the conference. Some participantsalso presented research on first and second language learning, while others discussed the relationshipbetween gesture and intonation. While most participants presented research on gesture and speech froman observer's perspective, be it in semiotics or pragmatics, some nevertheless focused on another importantaspect: the cognitive processes involved in language production and perception. Last but not least,participants also presented talks and posters on the computational analysis of gestures, whether involvingexternal devices (e.g. mocap, kinect) or concerning the use of specially-designed computer software forthe post-treatment of gestural data. Importantly, new links were made between semiotics and mocap data