Feature Extraction in Speech Signal Recognition
Objective
The objective of this research project on "Feature Extraction in Speech Signal Recognition" is to develop advanced and efficient techniques for extracting relevant features from speech signals to enhance the accuracy and robustness of automatic speech recognition (ASR) systems.
Abstract
Feature extraction in speech signal recognition plays a pivotal role in advancing the capabilities of automatic speech recognition (ASR) systems. In order to develop more sophisticated and efficient techniques, the utilization of Information Theory (IT) becomes paramount. By harnessing IT principles, ASR systems can extract salient features from speech signals, elevating both accuracy and robustness. These advanced methods enable the system to discern subtle nuances in spoken language, including phonetic variations and contextual cues, paving the way for improved performance across diverse linguistic contexts and environmental conditions. The integration of IT not only empowers ASR systems to decode spoken words more accurately but also enhances their adaptability, making them invaluable tools in applications ranging from voice assistants to transcription services and beyond. Consequently, feature extraction through Information Theory emerges as a critical frontier in the evolution of speech signal recognition technology.
Keywords: Automatic speech recognition; feature extraction; comparative study, Accuracy.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
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 an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation 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
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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.
