Emotions are fundamental features of human beings, impacting their perception and everyday activities such as communication and decision making. They are expressed through speech, facial expressions, gestures and other nonverbal cues. Speech emotion recognition is the process of analyzing vocal behavior, with emphasis on nonverbal aspects of speech. Difference of emotional states can be considered as one of the important evaluation criteria to measure the performance of cognition procedures, especially for the process of decision making and action tendency. Emotion plays a significant role in influencing motivation and focus of attention. Most of the previous studies on speech emotion recognition have normally used pattern recognition methods using extracted acoustic features (such as pitch, energy and Melfrequency filter banks, Mel-frequency cepstral coefficients (MFCCs) etc.) from audio files. Most of the speech emotion categorization techniques rely on the frequency-domain stationary methods like Fourier power spectrum. These methods have been strongly questioned for non-stationary aspects of signal. Not much has been done in this area by analyzing the non-stationary aspects of the speech signal. At DGRF a rigorous non-stationary methodology capable of categorization of speech signals of various emotions is done.

  • Non-Invasive Detection of Alzheimer’s Disease-Multifractality of Emotional Speech
    Emotion classification from vocal expressions is an important area of research. This study has been able to numerically quantify emotions based on the vocal expressions. This has opened up an area of study in the medical diagnosis of Alzheimer’s disease. Emotions are fundamental for human beings, impacting their perception and everyday activities such as communication […] keyboard_arrow_right
  • Speech Emotion Quantification with Chaos-based Modified Visibility Graph – Possible Precursor of Suicidal Tendency
    A modified version of the non-linear analysis technique formulated indigenously for the analysis of speech signals is introduced here as a method. Using this method, it has been shown that the parameter calculated using this method can distinctively classify speech signals spoken out of two elementary emotions namely ‘anger’ and ‘sadness’. This parameter is also […] keyboard_arrow_right