Voice Emotion Recognition Based on Color Histogram Features

Voice is the fastest and most efficient method of communication among humans. Researchers believe that it can also be considered the most efficient means of communication between humans and machines. Voice can, in addition to providing useful information, inform us about the emotional state of the person who is speaking. For many applications, being able to identify the emotion is crucial, as it allows the application to adapt to the user. In the case of human-robot interaction, recognizing the user's emotion allows the robot to be more empathetic during interactions. In addition to other methods, such as recognition of facial expressions and recognition through body expressions, recognition of emotions through speech can be used as an additional component in identification the user's emotional state. This work proposes the recognition of emotion through speech using an approach based on image processing of the voice audio signal spectrogram. Two new features based on color histograms are proposed. One thousand six hundred audio files with phrases considering four types of emotions (angry, happy, neutral and sad) were processed and classified. These phrases were spoken by women half by a 64 years old (Subject64) and the rest by a 26 years old one (Subject-26). These files are a subset of the TESS (Toronto Emotional Speech Set) dataset. When processing subject-26's voice, an precision of 94.40% and 91.90% was achieved in detecting neutral and sad emotions, respectively. When processing subject-64's voice, an precision of 97.00% was achieved for the angry emotion. The results obtained show the proposal great potential.