Abstract
Speech is a rich source of information which gives not only about what a speaker says, but also about what the speaker’s attitude is toward the listener and toward the topic under discussion—as well as the speaker’s own current state of mind. Recently increasing attention has been d irected to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. The focus of this research workis to enhance man machine interface by focusing on u ser’s speech emotion. This paper gives the results of the basic analysis on prosodic features and also compares the prosodic fe atures of, various types and degrees of emotional expressions in Tamil speech based on the auditory impressions between the two genders of speakers as well as listeners. The speech samples consist of “neutral” speech as well as speech with three types of emotions (“anger”, “joy”, and “sadness”) of three degrees (“light”, “medium”, and “strong”). A listening test is also being conducted using 300 speech samples uttered by students at the ages of 19-22 the ages of 19-22 years old. The features of prosodic parameters based on the emotional speech classified according to the auditory i mpressions of the subjects are analyzed. Analysis results suggest that prosodic features that identify their emotions and degrees are not only speakers’ gender dependent, but also listeners’ gender dependent.
Highlights
One can take advantage of the fact that changes in the autonomic nervous system indirectly alter speech, and use this information to produce systems capable of recognizing affect based on extracted features of speech
It has been difficult to find the features that describe the emotional content in speech
Speech samples were presented to the subjects in random order
Summary
One can take advantage of the fact that changes in the autonomic nervous system indirectly alter speech, and use this information to produce systems capable of recognizing affect based on extracted features of speech. Speech produced in a state of fear, anger or joy becomes faster, louder, precisely enunciated with a higher and wider pitch range. Other emotions such as tiredness, boredom or sadness, lead to slower, lower-pitched and slurred speech. Emotional speech processing recognizes the user's emotional state by analyzing speech patterns. It has been difficult to find the features that describe the emotional content in speech. It has not been reached a reliable set of features for discriminating emotional states in spontaneous speech [1]. Much of the work done to date has been focused on features related to prosodic aspects
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