The purpose of speech emotion recognition system is to differentiate the speaker's utterances into four emotional states namely happy, sad, anger and neutral. Automatic speech emotion recognition is an active research area in the field of human computer interaction (HCI) with wide range of applications. Extracted features of our project work are mainly related to statistics of pitch and energy as well as spectral features. Selected features are fed as input to Support Vector Machine (SVM) classifier. Two kernels linear and Gaussian radial basis function are tested with binary tree, one against one and one versus the rest classification strategies. The proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately and combining both, SAVEE database as well as with a self made database in Malayalam language containing samples of female only. Finally results for different combination of the features and on different databases are compared and explained. The highest accuracy is obtained with the feature combination of MFCC +Pitch+ Energy on both Malayalam emotional database (95.83%) and Berlin emotional database (75%) tested with binary tree using linear kernel.

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Many hardware efficient algorithms exists for hardware signal processing architecture. Among these algorithm is a set of shift-add algorithms collectively known as CORDIC (COrdinate Rotation for Digital Computers) for computing a wide range of functions including certain trigonometric, hyperbolic, linear and logarithmic functions. The paper compares the different CORDIC architectures with respect to their area, speed, and data throughput performance especially in three different major styles iterative, parallel and pipelined structures. All three designs were designed in VHDL, simulated using Modelsim simulator and Implemented using Xilinx FPGA synthesis and Synopsis ASIC synthesis tools.