Indian Institute of Technology Guwahati

About: Indian Institute of Technology Guwahati is a education organization based out in Guwahati, Assam, India. It is known for research contribution in the topics: Adsorption & Catalysis. The organization has 6933 authors who have published 17102 publications receiving 257351 citations.

Glutaraldehyde-Activated Chitosan Matrix for Immobilization of a Novel Cysteine Protease, Procerain B

Abstract: Proteases have several applications in the food industry. We report the immobilization of procerain B, a novel cysteine protease, on glutaraldehyde-activated chitosan beads through covalent attachment. Glutaraldehyde not only serves as a cross-linking agent but also links the procerain B on the surface of bead through primary amine group (either lysine side chain or N-terminal) by Schiff base linkage. Immobilized procerain B was characterized for optimum functional range and stability with respect to pH and temperature. The chitosan-immobilized procerain B has broad pH and thermal optima. The effects of substrate concentration and reusability of immobilized beads were also studied. It showed nearly 50% activity until the 10th use.

Sinusoidal model-based analysis and classification of stressed speech

Abstract: In this paper, a sinusoidal model has been proposed for characterization and classification of different stress classes (emotions) in a speech signal. Frequency, amplitude and phase features of the sinusoidal model are analyzed and used as input features to a stressed speech recognition system. The performances of sinusoidal model features are evaluated for recognition of different stress classes with a vector-quantization classifier and a hidden Markov model classifier. To find the effectiveness of these features for recognition of different emotions in different languages, speech signals are recorded and tested in two languages, Telugu (an Indian language) and English. Average stressed speech index values are proposed for comparing differences between stress classes in a speech signal. Results show that sinusoidal model features are successful in characterizing different stress classes in a speech signal. Sinusoidal features perform better compared to the linear prediction and cepstral features in recognizing the emotions in a speech signal.