Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

The Perception of Emotion from Acoustic Cues in Natural Speech

Knowledge of human perception of emotional speech is imperative for the development of emotion in speech recognition systems and emotional speech synthesis. Owing to the fact that there is a growing trend towards research on spontaneous, real-life data, the aim of the present thesis is to examine human perception of emotion in naturalistic speech. Although there are many available emotional speech corpora, most contain simulated expressions. Therefore, there remains a compelling need to obtain naturalistic speech corpora that are appropriate and freely available for research. In that regard, our initial aim was to acquire suitable naturalistic material and examine its emotional content based on listener perceptions. A web-based listening tool was developed to accumulate ratings based on large-scale listening groups. The emotional content present in the speech material was demonstrated by performing perception tests on conveyed levels of Activation and Evaluation. As a result, labels were determined that signified the emotional content, and thus contribute to the construction of a naturalistic emotional speech corpus. In line with the literature, the ratings obtained from the perception tests suggested that Evaluation (or hedonic valence) is not identified as reliably as Activation is. Emotional valence can be conveyed through both semantic and prosodic information, for which the meaning of one may serve to facilitate, modify, or conflict with the meaning of the other—particularly with naturalistic speech. The subsequent experiments aimed to investigate this concept by comparing ratings from perception tests of non-verbal speech with verbal speech. The method used to render non-verbal speech was low-pass filtering, and for this, suitable filtering conditions were determined by carrying out preliminary perception tests. The results suggested that nonverbal naturalistic speech provides sufficiently discernible levels of Activation and Evaluation. It appears that the perception of Activation and Evaluation is affected by low-pass filtering, but that the effect is relatively small. Moreover, the results suggest that there is a similar trend in agreement levels between verbal and non-verbal speech. To date it still remains difficult to determine unique acoustical patterns for hedonic valence of emotion, which may be due to inadequate labels or the incorrect selection of acoustic parameters. This study has implications for the labelling of emotional speech data and the determination of salient acoustic correlates of emotion

Multimodal emotion recognition

Reading emotions from facial expression and speech is a milestone in Human-Computer Interaction. Recent sensing technologies, namely the Microsoft Kinect Sensor, provide basic input modalities data, such as RGB imaging, depth imaging and speech, that can be used in Emotion Recognition. Moreover Kinect can track a face in real time and present the face fiducial points, as well as 6 basic Action Units (AUs). In this work we explore this information by gathering a new and exclusive dataset. This is a new opportunity for the academic community as well to the progress of the emotion recognition problem. The database includes RGB, depth, audio, fiducial points and AUs for 18 volunteers for 7 emotions. We then present automatic emotion classification results on this dataset by employing k-Nearest Neighbor, Support Vector Machines and Neural Networks classifiers, with unimodal and multimodal approaches. Our conclusions show that multimodal approaches can attain better results.Ler e reconhecer emoções de expressões faciais e verbais é um marco na Interacção Humana com um Computador. As recentes tecnologias de deteção, nomeadamente o sensor Microsoft Kinect, recolhem dados de modalidades básicas como imagens RGB, de informaçãode profundidade e defala que podem ser usados em reconhecimento de emoções. Mais ainda, o sensor Kinect consegue reconhecer e seguir uma cara em tempo real e apresentar os pontos fiduciais, assim como as 6 AUs – Action Units básicas. Neste trabalho exploramos esta informação através da compilação de um dataset único e exclusivo que representa uma oportunidade para a comunidade académica e para o progresso do problema do reconhecimento de emoções. Este dataset inclui dados RGB, de profundidade, de fala, pontos fiduciais e AUs, para 18 voluntários e 7 emoções. Apresentamos resultados com a classificação automática de emoções com este dataset, usando classificadores k-vizinhos próximos, máquinas de suporte de vetoreseredes neuronais, em abordagens multimodais e unimodais. As nossas conclusões indicam que abordagens multimodais permitem obter melhores resultados

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Emotion Recognition from Speech Signals and Perception of Music

This thesis deals with emotion recognition from speech signals. The feature extraction step shall be improved by looking at the perception of music. In music theory, different pitch intervals (consonant, dissonant) and chords are believed to invoke different feelings in listeners. The question is whether there is a similar mechanism between perception of music and perception of emotional speech. Our research will follow three stages. First, the relationship between speech and music at segmental and supra-segmental levels will be analyzed. Secondly, the encoding of emotions through music shall be investigated. In the third stage, a description of the most common features used for emotion recognition from speech will be provided. We will additionally derive new high-level musical features, which will lead us to an improvement of the recognition rate for the basic spoken emotions

Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert