A system for recognizing human emotions based on speech analysis and facial feature extraction: applications to Human-Robot Interaction

With the advance in Artificial Intelligence, humanoid robots start to interact with ordinary people based on the growing understanding of psychological processes. Accumulating evidences in Human Robot Interaction (HRI) suggest that researches are focusing on making an emotional communication between human and robot for creating a social perception, cognition, desired interaction and sensation. Furthermore, robots need to receive human emotion and optimize their behavior to help and interact with a human being in various environments. The most natural way to recognize basic emotions is extracting sets of features from human speech, facial expression and body gesture. A system for recognition of emotions based on speech analysis and facial features extraction can have interesting applications in Human-Robot Interaction. Thus, the Human-Robot Interaction ontology explains how the knowledge of these fundamental sciences is applied in physics (sound analyses), mathematics (face detection and perception), philosophy theory (behavior) and robotic science context. In this project, we carry out a study to recognize basic emotions (sadness, surprise, happiness, anger, fear and disgust). Also, we propose a methodology and a software program for classification of emotions based on speech analysis and facial features extraction. The speech analysis phase attempted to investigate the appropriateness of using acoustic (pitch value, pitch peak, pitch range, intensity and formant), phonetic (speech rate) properties of emotive speech with the freeware program PRAAT, and consists of generating and analyzing a graph of speech signals. The proposed architecture investigated the appropriateness of analyzing emotive speech with the minimal use of signal processing algorithms. 30 participants to the experiment had to repeat five sentences in English (with durations typically between 0.40 s and 2.5 s) in order to extract data relative to pitch (value, range and peak) and rising-falling intonation. Pitch alignments (peak, value and range) have been evaluated and the results have been compared with intensity and speech rate. The facial feature extraction phase uses the mathematical formulation (B\ue9zier curves) and the geometric analysis of the facial image, based on measurements of a set of Action Units (AUs) for classifying the emotion. The proposed technique consists of three steps: (i) detecting the facial region within the image, (ii) extracting and classifying the facial features, (iii) recognizing the emotion. Then, the new data have been merged with reference data in order to recognize the basic emotion. Finally, we combined the two proposed algorithms (speech analysis and facial expression), in order to design a hybrid technique for emotion recognition. Such technique have been implemented in a software program, which can be employed in Human-Robot Interaction. The efficiency of the methodology was evaluated by experimental tests on 30 individuals (15 female and 15 male, 20 to 48 years old) form different ethnic groups, namely: (i) Ten adult European, (ii) Ten Asian (Middle East) adult and (iii) Ten adult American. Eventually, the proposed technique made possible to recognize the basic emotion in most of the cases

A model of sonority based on pitch intelligibility

Synopsis: Sonority is a central notion in phonetics and phonology and it is essential for generalizations related to syllabic organization. However, to date there is no clear consensus on the phonetic basis of sonority, neither in perception nor in production. The widely used Sonority Sequencing Principle (SSP) represents the speech signal as a sequence of discrete units, where phonological processes are modeled as symbol manipulating rules that lack a temporal dimension and are devoid of inherent links to perceptual, motoric or cognitive processes. The current work aims to change this by outlining a novel approach for the extraction of continuous entities from acoustic space in order to model dynamic aspects of phonological perception. It is used here to advance a functional understanding of sonority as a universal aspect of prosody that requires pitch-bearing syllables as the building blocks of speech. This book argues that sonority is best understood as a measurement of pitch intelligibility in perception, which is closely linked to periodic energy in acoustics. It presents a novel principle for sonority-based determinations of well-formedness – the Nucleus Attraction Principle (NAP). Two complementary NAP models independently account for symbolic and continuous representations and they mostly outperform SSP-based models, demonstrated here with experimental perception studies and with a corpus study of Modern Hebrew nouns. This work also includes a description of ProPer (Prosodic Analysis with Periodic Energy). The ProPer toolbox further exploits the proposal that periodic energy reflects sonority in order to cover major topics in prosodic research, such as prominence, intonation and speech rate. The book is finally concluded with brief discussions on selected topics: (i) the phonotactic division of labor with respect to /s/-stop clusters; (ii) the debate about the universality of sonority; and (iii) the fate of the classic phonetics–phonology dichotomy as it relates to continuity and dynamics in phonology

Melody as Prosody: Toward a Usage-Based Theory of Music

MELODY AS PROSODY: TOWARD A USAGE-BASED THEORY OF MUSIC Thomas M. Pooley Gary A. Tomlinson Rationalist modes of inquiry have dominated the cognitive science of music over the past several decades. This dissertation contests many rationalist assumptions, including its core tenets of nativism, modularity, and computationism, by drawing on a wide range of evidence from psychology, neuroscience, linguistics, and cognitive music theory, as well as original data from a case study of Zulu song prosody. An alternative biocultural approach to the study of music and mind is outlined that takes account of musical diversity by attending to shared cognitive mechanisms. Grammar emerges through use, and cognitive categories are learned and constructed in particular social contexts. This usage-based theory of music shows how domain-general cognitive mechanisms for patterning-finding and intention-reading are crucial to acquisition, and how Gestalt principles are invoked in perception. Unlike generative and other rationalist approaches that focus on a series of idealizations, and the cognitive `competences\u27 codified in texts and musical scores, the usage-based approach investigates actual performances in everyday contexts by using instrumental measures of process. The study focuses on song melody because it is a property of all known musics. Melody is used for communicative purposes in both song and speech. Vocalized pitch patterning conveys a wide range of affective, propositional, and syntactic information through prosodic features that are shared by the two domains. The study of melody as prosody shows how gradient pitch features are crucial to the design and communicative functions of song melodies. The prosodic features shared by song and speech include: speech tone, intonation, and pitch-accent. A case study of ten Zulu memulo songs shows that pitch is not used in the discrete or contrastive fashion proposed by many cognitive music theorists and most (generative) phonologists. Instead there are a range of pitch categories that include pitch targets, glides, and contours. These analyses also show that song melody has a multi-dimensional pitch structure, and that it is a dynamic adaptive system that is irreducible in its complexity

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Learning words and learning sounds : Advances in language development

Phonological development is sometimes seen as a process of learning sounds, or forming phonological categories, and then combining sounds to build words, with the evidence taken largely from studies demonstrating ‘perceptual narrowing’ in infant speech perception over the first year of life. In contrast, studies of early word production have long provided evidence that holistic word learning may precede the formation of phonological categories. In that account, children begin by matching their existing vocal patterns to adult words, with knowledge of the phonological system emerging from the network of related word forms. Here I review evidence from production and then consider how the implicit and explicit learning mechanisms assumed by the complementary memory systems model might be understood as reconciling the two approaches

Advances in Subspace-based Solutions for Diarization in the Broadcast Domain

La motivación de esta tesis es la necesidad de soluciones robustas al problema de diarización. Estas técnicas de diarización deben proporcionar valor añadido a la creciente cantidad disponible de datos multimedia mediante la precisa discriminación de los locutores presentes en la señal de audio. Desafortunadamente, hasta tiempos recientes este tipo de tecnologías solamente era viable en condiciones restringidas, quedando por tanto lejos de una solución general. Las razones detrás de las limitadas prestaciones de los sistemas de diarización son múltiples. La primera causa a tener en cuenta es la alta complejidad de la producción de la voz humana, en particular acerca de los procesos fisiológicos necesarios para incluir las características discriminativas de locutor en la señal de voz. Esta complejidad hace del proceso inverso, la estimación de dichas características a partir del audio, una tarea ineficiente por medio de las técnicas actuales del estado del arte. Consecuentemente, en su lugar deberán tenerse en cuenta aproximaciones. Los esfuerzos en la tarea de modelado han proporcionado modelos cada vez más elaborados, aunque no buscando la explicación última de naturaleza fisiológica de la señal de voz. En su lugar estos modelos aprenden relaciones entre la señales acústicas a partir de un gran conjunto de datos de entrenamiento. El desarrollo de modelos aproximados genera a su vez una segunda razón, la variabilidad de dominio. Debido al uso de relaciones aprendidas a partir de un conjunto de entrenamiento concreto, cualquier cambio de dominio que modifique las condiciones acústicas con respecto a los datos de entrenamiento condiciona las relaciones asumidas, pudiendo causar fallos consistentes en los sistemas.Nuestra contribución a las tecnologías de diarización se ha centrado en el entorno de radiodifusión. Este dominio es actualmente un entorno todavía complejo para los sistemas de diarización donde ninguna simplificación de la tarea puede ser tenida en cuenta. Por tanto, se deberá desarrollar un modelado eficiente del audio para extraer la información de locutor y como inferir el etiquetado correspondiente. Además, la presencia de múltiples condiciones acústicas debido a la existencia de diferentes programas y/o géneros en el domino requiere el desarrollo de técnicas capaces de adaptar el conocimiento adquirido en un determinado escenario donde la información está disponible a aquellos entornos donde dicha información es limitada o sencillamente no disponible.Para este propósito el trabajo desarrollado a lo largo de la tesis se ha centrado en tres subtareas: caracterización de locutor, agrupamiento y adaptación de modelos. La primera subtarea busca el modelado de un fragmento de audio para obtener representaciones precisas de los locutores involucrados, poniendo de manifiesto sus propiedades discriminativas. En este área se ha llevado a cabo un estudio acerca de las actuales estrategias de modelado, especialmente atendiendo a las limitaciones de las representaciones extraídas y poniendo de manifiesto el tipo de errores que pueden generar. Además, se han propuesto alternativas basadas en redes neuronales haciendo uso del conocimiento adquirido. La segunda tarea es el agrupamiento, encargado de desarrollar estrategias que busquen el etiquetado óptimo de los locutores. La investigación desarrollada durante esta tesis ha propuesto nuevas estrategias para estimar el mejor reparto de locutores basadas en técnicas de subespacios, especialmente PLDA. Finalmente, la tarea de adaptación de modelos busca transferir el conocimiento obtenido de un conjunto de entrenamiento a dominios alternativos donde no hay datos para extraerlo. Para este propósito los esfuerzos se han centrado en la extracción no supervisada de información de locutor del propio audio a diarizar, sinedo posteriormente usada en la adaptación de los modelos involucrados.<br /