A combined cepstral distance method for emotional speech recognition

Affective computing is not only the direction of reform in artificial intelligence but also exemplification of the advanced intelligent machines. Emotion is the biggest difference between human and machine. If the machine behaves with emotion, then the machine will be accepted by more people. Voice is the most natural and can be easily understood and accepted manner in daily communication. The recognition of emotional voice is an important field of artificial intelligence. However, in recognition of emotions, there often exists the phenomenon that two emotions are particularly vulnerable to confusion. This article presents a combined cepstral distance method in two-group multi-class emotion classification for emotional speech recognition. Cepstral distance combined with speech energy is well used as speech signal endpoint detection in speech recognition. In this work, the use of cepstral distance aims to measure the similarity between frames in emotional signals and in neutral signals. These features are input for directed acyclic graph support vector machine classification. Finally, a two-group classification strategy is adopted to solve confusion in multi-emotion recognition. In the experiments, Chinese mandarin emotion database is used and a large training set (1134 + 378 utterances) ensures a powerful modelling capability for predicting emotion. The experimental results show that cepstral distance increases the recognition rate of emotion sad and can balance the recognition results with eliminating the over fitting. And for the German corpus Berlin emotional speech database, the recognition rate between sad and boring, which are very difficult to distinguish, is up to 95.45%

Multi-stage Classification of Emotional Speech Motivated by a Dimensional Emotion Model

International audienceThis paper deals with speech emotion analysis within the context of increasing awareness of the wide application potential of affective computing. Unlike most works in the literature which mainly rely on classical frequency and energy based features along with a single global classifier for emotion recognition, we propose in this paper some new harmonic and Zipf based features for better speech emotion characterization in the valence dimension and a multi-stage classification scheme driven by a dimensional emotion model for better emotional class discrimination. Experimented on the Berlin dataset with 68 features and six emotion states, our approach shows its effectiveness, displaying a 68.60% classification rate and reaching a 71.52% classification rate when a gender classification is first applied. Using the DES dataset with five emotion states, our approach achieves an 81% recognition rate when the best performance in the literature to our knowledge is 76.15% on the same dataset

Anotação Emocional de Filmes com Gamificação

Tese de mestrado, Engenharia Informática (Sistema de Informação) Universidade de Lisboa, Faculdade de Ciências, 2022O entretenimento esteve sempre presente nas atividades humanas, satisfazendo necessidades e desempenhando um papel na vida dos indivíduos e das comunidades. Em particular, os filmes e os jogos têm um forte impacto emocional sobre nós; os primeiros com o seu rico conteúdo multimédia e a própria história e os segundos tendem a desafiar-nos e a cativar-nos a enfrentar desafios e, espera-se, alcançar experiências e resultados gratificantes. Nesta dissertação apresentamos uma aplicação web desenvolvida no laboratório de investigação LASIGE (DI-FCUL), concebida e desenvolvida para aceder a filmes com base no impacto emocional, com o foco na anotação emocional de filmes, utilizando diferentes representações emocionais e elementos de gamificação no sentido de incentivar mais os utilizadores nestas tarefas, para além das suas motivações intrínsecas. Estas anotações, com abordagens de Machine Learning, podem ajudar a enriquecer a classificação emocional dos filmes e o seu impacto nos utilizadores, ajudando mais tarde a encontrar filmes baseados nesse impacto. Podem também ser guardadas como notas pessoais, num diário (Personal Journal), onde os utilizadores registam os filmes que mais apreciam, e que podem rever e até mesmo comparar ao longo da sua jornada. Apresentam-se também os dois momentos de avaliação com grupos de participantes, permitindo avaliar e aprender sobre a utilidade, usabilidade e a experiência do utilizador com a aplicação, identificando as características e direções mais promissoras para os futuros melhoramentos e desenvolvimentos.Entertainment has always been present in human activities, satisfying needs and playing a role in the lives of individuals and communities. In particular, movies and games have a strong emotional impact on us; the first with their rich multimedia content and the story itself, and the second tend to challenge and entice us to face challenges and hopefully achieve rewarding experiences and results. In this dissertation we present a web application developed at LASIGE research lab (DIFCUL), designed and developed to access movies based on emotional impact, focusing on emotional annotation of movies, using different emotional representations and gamification elements in order to further encourage users in these tasks, beyond their intrinsic motivations. These annotations, with machine learning approaches, can help enrich the emotional classification of films and their impact on users, later helping to find films based on that impact. They can also be kept as personal notes, in a diary (Personal Journal), where users record the movies they enjoy most, and which they can review and even compare along their journey. The two evaluation moments with groups of participants are also presented, allowing us to evaluate and learn about the usefulness, usability, and the user experience with the application, identifying the most promising features and directions for future improvements and developments