This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
Corporate Social Responsibility (CSR) has been on the agenda in India for quite some time. The private sector is generally more active in this area than the Governmental and public sector; and the situation is slowly changing. The concept of CSR is finally coming out of the purview of ‘doing social good’ and is fast becoming a ‘business necessity’. This approach to CSR is gaining ground and many corporate houses are realizing the benefits they get in business by extending help to their workers, local community and society at large. There are many corporate companies and voluntary foundations in India who are doing commendable work in education, health, environment and energy under CSR. CSR can also be extended to public libraries, because public libraries are considered as ‘social institutions’ providing basic information for social, economic and cultural development of citizens; and to sustain lifelong learning for students. Public libraries play a vital role in disseminating existing knowledge and promote the creation of new knowledge.
This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
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