Recognizing Emotional State Changes Using Speech Processing

Research on understanding human emotions in speech, seeks to find out utterance mood by analyzing cognitive attributes extracted from acoustical speech signal. Speech contains rich patterns which can be altered by mood of a speaker. This paper explores speech from database and long-term speech recordings to analyze of mood changing in individual speaker during long-term speech. We introduce a learning method based on statistical model to classify emotional states and moods of utterance, and also track its changes. With this object, the perceptual backgrounds of the individual speaker are analyzed, and then classified during the speech to extract patterns, which is embedded in speech signal. The proposed method, classifies emotions of the utterance in seven standard classes including, happiness, anger, boredom, fear, disgust, neutral and sadness. To this end, we call the standard speech corpus database, the EmoDB for the training phase of this approach. Thus, when pre-processing tasks done, the speech patterns and meaningful attributes have extracted by the MFCC method and selected by SFS method, and then we apply a statistical classification approach, LGMM, to categorize obtained features, and finally illustrate changes trend of the emotional states.