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In this work, we are attempting emotion classification in view of synthesizing story speech. We are proposing emotion-specific text features (ESF) for classifying sentences from children stories into five different emotion categories: happy, sad, anger, fear and neutral. ESF is a five dimensional feature vector, where each dimension corresponds to weight of the sentence according to each emotion class...
The main objective of this work is to classify Hindi stories into three genres: fable, folk-tale and legend. In this paper, we are proposing a framework for story classification using keyword and Part-of-speech (POS) based features. Keyword based features like Term Frequency (TF) and Term Frequency Inverse Document Frequency (TFIDF) are used. Effect of POS tags like Noun, Pronoun, Adjective etc.,...
The main objective of this work is to classify Hindi and Telugu stories based on their structure into three genres: Fable, Folk-tale and Legend. In this work, each story is divided into three parts: (i) introduction, (ii) main and (iii) climax. The objective of this work is to explore how story genre information is embedded in different parts of the story. We are proposing a framework for story classification...
The basic goal of this work is to develop a Consonant-Vowel Recognition System (CVRS) for determining a sequence of Consonant-Vowel (CV) units present in a given speech utterance. In this work, we are focusing on developing CVRSs for Indian languages namely Bengali and Odia. This framework of developing CVRSs can be extended to any Indian languages. We have developed two separate CVRSs for Bengali...
This paper explores the GMM-SVM combined approach for Text-Independent speaker verification in noisy environment. In recent years supervectors constructed by stacking the means of adapted Gaussian Mixture Models (GMMs) have been used successfully for deriving sequence kernels. Support Vector Machines (SVMs) trained using such kernels provide further improvement in classification accuracy. Analysis...
In this paper, spectral and prosodic features are explored for recognition of infant cry. Different types of infant cries considered in this work are wet-diaper, hunger and pain. In this work, mel-frequency cepstral coefficients (MFCC) are used to represent the spectral information, and short-time frame energies (STE) and pause duration are used for representing the prosodic information. Support Vector...
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