Support Vector Machine Classification of Stress Types in Speech
Journal Title: IOSR journal of VLSI and Signal Processing - Year 2018, Vol 8, Issue 2
Abstract
Speech of human beings is the reflection of the state of mind. Proper evaluation of these speech signals into stress types is necessary in order to ensure that the person is in a healthy state of mind. In this work we propose a SVM classifier for speech stress classification algorithm, with sophisticated feature extraction techniques as Mel Frequency Cepstral Coefficients (MFCC). The SVM algorithm assists the system to learn the speech patterns in real time and self-train itself in order to improve the classification accuracy of the overall system. The proposed system is suitable for real time speech and is language and word independent.
Authors and Affiliations
Mrs. N. P. Dhole1 ,, Dr. S. N. Kale2
Keywords
Related Articles
- EP ID EP412812
- DOI -
- Views 164
- Downloads 0
A Study onFace Detection and Image Steganographyusing Discrete Wavelet Transform
There are many methods available for secret data hiding and transfer, among which steganography stands out in various aspects. Steganography is used for secret data transfer in a cover object which may be an image, video...
Design of Energy Efficient and Size Reduced Scram Cell
Semiconductor memories are a vital component of essentially any state of the art digital circuits. One among them is the Static Random Access Memory (SRAM). The dynamic power consumption contributes to about 80% of the t...
Design of Viterbi Decoder for Speech to Text Conversion Application using ACS Architecture
The Viterbi algorithm is generally used in decoding convolutional codes utilized in communications. In this paper Viterbi decoder and convolution encoder is designed, used in speech to text conversion.We can utilize thre...
Basic Topologies of MOS Single-Stage Amplifiers. DC Analysis For Maximum Input-Voltage Swing And Amplification
Review basic MOS single-stage amplifier topologies and perform comparative DC analysis in order to determine for each topology the pair: (maximum input voltage swing, amplification). The results will be used in following...
Detection of Alzhemier’s Disease Using Fractional Edge Detection
The work consist of two phases. The first phase of the work aims at finding out the optimized value of the fraction used in fractional filtering for image enhancement techniques in digital image processing. The work is d...