Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 2 | Issue - 4 | december 2020
Published
19 February, 2021
Recently, the application of voice-controlled interfaces plays a major role in many real-time environments such as a car, smart home and mobile phones. In signal processing, the accuracy of speech recognition remains a thought-provoking challenge. The filter designs assist speech recognition systems in terms of improving accuracy by parameter tuning. This task is some degree of form filter's narrowed specifications which lead to complex nonlinear problems in speech recognition. This research aims to provide analysis on complex nonlinear environment and exploration with recent techniques in the combination of statistical-based design and Support Vector Machine (SVM) based learning techniques. Dynamic Bayes network is a dominant technique related to speech processing characterizing stack co-occurrences. This method is derived from mathematical and statistical formalism. It is also used to predict the word sequences along with the posterior probability method with the help of phonetic word unit recognition. This research involves the complexities of signal processing that it is possible to combine sentences with various types of noises at different signal-to-noise ratios (SNR) along with the measure of comparison between the two techniques.
KeywordsSpeech Recognition Machine Learning Signal Process filtering techniques
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