Recognizing emotions from speech is a tuff task as we are not aware of the features which will accurately classify the emotions. This paper is an approach to show which speech feature classifies the emotions more accurately. The features compared here are Pitch and Formant while the classifier used is Linear Discriminant Analysis (LDA). The database used in this experiment was developed using 50 male and 50 female Marathi speaking native speakers. The emotions used here are Neutral, Happy, Sad, Surprise and Boredom. At the end of the experiment it was observed that formant recognized the emotions very efficiently and accurately with respect to that of energy.

Abstract-Prosody modification involves changing the pitch and duration of speech without affecting the message and naturalness. This paper proposes a method for prosody (pitch and duration) modification using the instants of significant excitation of the vocal tract system during the production of speech. The instants of significant excitation correspond to the instants of glottal closure (

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This letter proposes a time-effective method for determining the instants of significant excitation in speech signals. The instants of significant excitation correspond to the instants of glottal closure (epochs) in the case of voiced speech, and to some random excitations like onset of burst in the case of nonvoiced speech. The proposed method consists of two phases: the first phase determines the approximate epoch locations using the Hilbert envelope of the linear prediction residual of the speech signal. The second phase determines the accurate locations of the instants of significant excitation by computing the group delay around the approximate epoch locations derived from the first phase. The accuracy in determining the instants of significant excitation and the time complexity of the proposed method is compared with the group delay based approach.

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