A Robust Speaker Identification System for Natural
Objective
Analyze audio signals to extract features, compare classifiers, assess accuracy, and visualize results for normal and whispered speech.
Abstract
Speaker Identification, using both speech and whisper, is an emerging research topic. The main challenge lies in improving the robustness of the system in highly noisy environment. In this paper, different identification algorithms for both normal and whispered speech have been compared to check the robustness. Mel frequency cepstral coefficient (MFCC) method, Gabor filter-bank methods and an Empirical Mode Decomposition (EMD) based AM-FM approach have been employed for feature extraction. The extracted features have been classified with various classifiers such as Support Vector Machine, Fine K Nearest Neighbor (KNN) and Weighted KNN and all will be compared with our proposed classifier known as Random Forest Classifier. A database of 16 subjects has been created, both in normal, as well as in whispered mode. It is observed that the separate Gabor filter-bank method provides the best accuracy (98.1% with Fine KNN) for whispered speech, and the AM-FM approach offers the best accuracy (98.9% with Fine KNN) for normal speech. The proposed method will be compared with existing Decision Tree and will produce better results.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Communication?
· About Communication
· Introduction to Communication
· How Communication Works?
· Importing the System Design, Characterization and Visualization
· Analyzing of BER tool
· Analyzing of Error Rate Test Console
· Generation of WSN
· WSN network creation
· Nodes Communication
· Clustering
· Routing
· Convolutional
· Equalization and Synchronization etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
