Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Multi-Sensory Emotion Recognition with Speech and Facial Expression

Emotion plays an important role in human beings’ daily lives. Understanding emotions and recognizing how to react to others’ feelings are fundamental to engaging in successful social interactions. Currently, emotion recognition is not only significant in human beings’ daily lives, but also a hot topic in academic research, as new techniques such as emotion recognition from speech context inspires us as to how emotions are related to the content we are uttering. The demand and importance of emotion recognition have highly increased in many applications in recent years, such as video games, human computer interaction, cognitive computing, and affective computing. Emotion recognition can be done from many sources including text, speech, hand, and body gesture as well as facial expression. Presently, most of the emotion recognition methods only use one of these sources. The emotion of human beings changes every second and using a single way to process the emotion recognition may not reflect the emotion correctly. This research is motivated by the desire to understand and evaluate human beings’ emotion from multiple ways such as speech and facial expressions. In this dissertation, multi-sensory emotion recognition has been exploited. The proposed framework can recognize emotion from speech, facial expression, and both of them. There are three important parts in the design of the system: the facial emotion recognizer, the speech emotion recognizer, and the information fusion. The information fusion part uses the results from the speech emotion recognition and facial emotion recognition. Then, a novel weighted method is used to integrate the results, and a final decision of the emotion is given after the fusion. The experiments show that with the weighted fusion methods, the accuracy can be improved to an average of 3.66% compared to fusion without adding weight. The improvement of the recognition rate can reach 18.27% and 5.66% compared to the speech emotion recognition and facial expression recognition, respectively. By improving the emotion recognition accuracy, the proposed multi-sensory emotion recognition system can help to improve the naturalness of human computer interaction

Deep Learning-Based Speech Emotion Recognition Using Librosa

Speech Emotion Recognition is a challenge of computational paralinguistic and speech processing that tries to identify and classify the emotions expressed in spoken language. The objective is to infer from a speaker's speech patterns, such as prosody, pitch, and rhythm, their emotional state, such as happiness, rage, sadness, or frustration. In the modern world, one of the most crucial marketing tactics is emotion detection. For a person, you might tailor several things in order to best fit their interests. Due to this, we made the decision to work on a project where we could identify a person's emotions based just on their speech, allowing us to handle a variety of AI-related applications. Examples include the ability of call centers to play music during tense exchanges. Another example might be a smart automobile that slows down when someone is scared or furious. In Python, we processed and extracted features from the audio files using the Librosa module. A Python library for audio and music analysis is called Librosa. It offers the fundamental components required to develop systems for retrieving music-related information. Because of this, there is a lot of potential for this kind of application in the market that would help businesses and ensure customer safety