An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Multimodal Speech Emotion Recognition Using Audio and Text

Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, we propose a novel deep dual recurrent encoder model that utilizes text data and audio signals simultaneously to obtain a better understanding of speech data. As emotional dialogue is composed of sound and spoken content, our model encodes the information from audio and text sequences using dual recurrent neural networks (RNNs) and then combines the information from these sources to predict the emotion class. This architecture analyzes speech data from the signal level to the language level, and it thus utilizes the information within the data more comprehensively than models that focus on audio features. Extensive experiments are conducted to investigate the efficacy and properties of the proposed model. Our proposed model outperforms previous state-of-the-art methods in assigning data to one of four emotion categories (i.e., angry, happy, sad and neutral) when the model is applied to the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.Comment: 7 pages, Accepted as a conference paper at IEEE SLT 201

Emotion recognition: recognition of emotions through voice

Dissertação de mestrado integrado em Informatics EngineeringAs the years go by, the interaction between humans and machines seems to gain more and more importance for many different reasons, whether it's taken into consideration personal or commercial use. On a time where technology is reaching many parts of our lives, it's important to keep thriving for a healthy progress and help not only to improve but also to maintain the benefits that everyone gets from it. This relationship can be tackled through many points, but here the focus will be on the mind. Emotions are still a mystery. The concept itself brings up serious questions because of its complex nature. Till the date, scientists still struggle to understand it, so it's crucial to pave the right path for the growth on technology on the aid of such topic. There is some consensus on a few indicators that provide important insights on mental state, like words used, facial expressions, voice. The context of this work is on the use of voice and, based on the field of Automatic Speech Emotion Recognition, it is proposed a full pipeline of work with a wide scope by resorting to sound capture and signal processing software, to learning and classifying through algorithms belonging on the Semi Supervised Learning paradigm and visualization techniques for interpretation of results. For the classification of the samples,using a semi-supervised approach with Neural Networks represents an important setting to try alleviating the dependency of human labelling of emotions, a task that has proven to be challenging and, in many cases, highly subjective, not to mention expensive. It is intended to rely mostly on empiric results more than theoretical concepts due to the complexity of the human emotions concept and its inherent uncertainty, but never to disregard prior knowledge on the matter.À medida que os anos passam, a interacção entre indivíduos e máquinas tem vindo a ganhar maior importância por várias razões, quer seja para uso pessoal ou comercial. Numa altura onde a tecnologia está a chegar a várias partes das nossas vidas, é importante continuar a perseguir um progresso saudável e ajudar não só a melhorar mas também manter os benefícios que todos recebem. Esta relação pode ser abordada por vários pontos, neste trabalho o foco está na mente. Emoções são um mistério. O próprio conceito levanta questões sobre a sua natureza complexa. Até aos dias de hoje, muitos cientistas debatem-se para a compreender, e é crucial que um caminho apropriado seja criado para o crescimento de tecnologia na ajuda da compreensão deste assunto. Existe algum consenso sobre indicadores que demonstram pistas importantes sobre o estado mental de um sujeito, como palavras, expressões faciais, voz. O conteúdo deste trabalho foca-se na voz e, com base no campo de Automatic Speech Emotion Recognition, é proposto uma sequência de procedimentos diversificados, ao optar por software de captura de som e processamento de sinais, aprendizagem e classificação através de algoritmos de Aprendizagem Semi Supervisionada e técnicas de visualização para interpretar resultados. Para a classificação de amostras, o uso de uma abordagem Semi Supervisionada com redes neuronais representam um procedimentos importante para tentar combater a alta dependência da anotação de amostras de emoções humanas, uma tarefa que se demonstra ser árdua e, em muitos casos, altamente subjectiva, para não dizer cara. A intenção é estabelecer raciocínios baseados em factores experimentais, mais que teóricos, devido à complexidade do conceito de emoções humanas e à sua incerteza associada, mas tendo sempre em conta conhecimento já estabelecido no assunto