I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

Bite weight prediction from acoustic recognition of chewing

Automatic dietary monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This paper presents an approach to predict weight of individual bites taken. We utilize a pattern recognition procedure to spot chewing cycles and food type in continuous data from an ear-pad chewing sound sensor. The recognized information is used to predict bite weight. We present our recognition procedure and demonstrate its operation on a set of three selected foods of different bite weights. Our evaluation is based on chewing sensor data of eight healthy study participants performing 504 habitual bites in total. The sound-based chewing recognition achieved recalls of 80% at 60%-70% precision. Food classification of chewing sequences resulted in an average accuracy of 94%. In total, 50 variables were derived from the chewing microstructure, and were analyzed for correlations between chewing behavior and bite weight. A subset of four variables was selected to predict bite weight using linear food-specific models. Mean weight prediction error was lowest for apples (19.4%) and largest for lettuce (31%) using the sound-based recognition. We conclude that bite weight prediction using acoustic chewing recordings is a feasible approach for solid foods, and should be further investigated

An Online Attachment Style Recognition System Based on Voice and Machine Learning

Attachment styles are known to have significant associations with mental and physical health. Specifically, insecure attachment leads individuals to higher risk of suffering from mental disorders and chronic diseases. The aim of this study is to develop an attachment recognition model that can distinguish between secure and insecure attachment styles from voice recordings, exploring the importance of acoustic features while also evaluating gender differences. A total of 199 participants recorded their responses to four open questions intended to trigger their attachment system using a web-based interrogation system. The recordings were processed to obtain the standard acoustic feature set eGeMAPS, and recursive feature elimination was applied to select the relevant features. Different supervised machine learning models were trained to recognize attachment styles using both gender-dependent and gender-independent approaches. The gender-independent model achieved a test accuracy of 58.88%, whereas the gender-dependent models obtained 63.88% and 83.63% test accuracy for women and men respectively, indicating a strong influence of gender on attachment style recognition and the need to consider them separately in further studies. These results also demonstrate the potential of acoustic properties for remote assessment of attachment style, enabling fast and objective identification of this health risk factor, and thus supporting the implementation of large-scale mobile screening systems

Speech emotion recognition through statistical classification

O propósito desta dissertação é a discussão do reconhecimento de emoção na voz. Para este fim, criou-se uma base de dados validada de discurso emocional simulado Português, intitulada European Portuguese Emotional Discourse Database (EPEDD) e foram operados algoritmos de classificação estatística nessa base de dados. EPEDD é uma base de dados simulada, caracterizada por pequenos discursos (5 frases longas, 5 frases curtas e duas palavras), todos eles pronunciados por 8 atores—ambos os sexos igualmente representados—em 9 diferentes emoções (raiva, alegria, nojo, excitação, apatia, medo, surpresa, tristeza e neutro), baseadas no modelo de emoções de Lövheim. Concretizou-se uma avaliação de 40% da base de dados por avaliadores inexperientes, filtrando 60% dos pequenos discursos, com o intuito de criar uma base de dados validada. A base de dados completa contem 718 instâncias, enquanto que a base de dados validada contém 116 instâncias. A qualidade média de representação teatral, numa escala de a 5 foi avaliada como 2,3. A base de dados validada é composta por discurso emocional cujas emoções são reconhecidas com uma taxa média de 69,6%, por avaliadores inexperientes. A raiva tem a taxa de reconhecimento mais elevada com 79,7%, enquanto que o nojo, a emoção cuja taxa de reconhecimento é a mais baixa, consta com 40,5%. A extração de características e a classificação estatística foi realizada respetivamente através dos softwares Opensmile e Weka. Os algoritmos foram operados na base dados original e na base de dados avaliada, tendo sido obtidos os melhores resultados através de SVMs, respetivamente com 48,7% e 44,0%. A apatia obteve a taxa de reconhecimento mais elevada com 79,0%, enquanto que a excitação obteve a taxa de reconhecimento mais baixa com 32,9%.The purpose of this dissertation is to discuss speech emotion recognition. It was created a validated acted Portuguese emotional speech database, named European Portuguese Emotional Discourse Database (EPEDD), and statistical classification algorithms have been applied on it. EPEDD is an acted database, featuring 12 utterances (2 single-words, 5 short sentences and 5 long sentences) per actor and per emotion, 8 actors, both genders equally represented, and 9 emotions (anger, joy, disgust, excitement, fear, apathy, surprise, sadness and neutral), based on Lövheim’s emotion model. We had 40% of the database evaluated by unexperienced evaluators, enabling us to produce a validated one, filtering 60% of the evaluated utterances. The full database contains 718 instances, while the validated one contains 116 instances. The average acting quality of the original database was evaluated, in a scale from 1 to 5, as 2,3. The validated database is composed by emotional utterances that have their emotions recognized on average at a 69,6% rate, by unexperienced judges. Anger had the highest recognition rate at 79,7%, while disgust had the lowest recognition rate at 40,5%. Feature extraction and statistical classification algorithms were performed respectively applying Opensmile and Weka software. Statistical classification algorithms operated in the full database and in the validated one, best results being obtained by SVMs, respectively the emotion recognition rates being 48,7% and 44,0%. Apathy had the highest recognition rate: 79.0%, while excitement had the lowest emotion recognition rate: 32.9%

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

Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review

Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research has been conducted to detect stress during public speaking in real time using voice analysis. In this context, the current review showed that the application of algorithms was not properly explored and helped identify the main obstacles in creating a suitable testing environment while accounting for current complexities and limitations. In this paper, we present our main idea and propose a stress detection computational algorithmic model that could be integrated into a Virtual Reality (VR) application to create an intelligent virtual audience for improving public speaking skills. The developed model, when integrated with VR, will be able to detect excessive stress in real time by analysing voice features correlated to physiological parameters indicative of stress and help users gradually control excessive stress and improve public speaking performanceComment: 41 pages, 7 figures, 4 table