CNN Based Speaker Recognition in Language and Text-Independent Small-Scale System
Objective
This project helps to identify the speaker recognition in multiple languages and based on the speaker identification the home appliances are automated
Abstract
Speech processing has emerged as one of the important application area of digital signal processing. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. To remove the noise from the audio signal a low pass filter is utilized.
Feature vectors from speech are extracted by using Mel-frequency Cepstral coefficients (MFCC) which carry the speaker's identity characteristics. These features are used to train the network. In this paper implementation we are using a random forest network. This network gives the classified audio signal as output with more accuracy which is about 95-100% than existing methods. Here, the classified output is then converted into the speech.
Keywords-Speaker Recognition, Neural Network, Voice Sample, Language-Independent Speaker Recognition, Independent Speaker Recognition System.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware & Software Requirements:
Software: Matlab 2018a 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 Math Works 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:
- Acquisition
- Image enhancement
- Image restoration
- Color image processing
- Image compression
- Morphological processing
- Segmentation etc.,
- About Artificial Intelligence (AI)
- About Machine Learning
- About Deep Learning
- About layers in AI (input, hidden and output layers)
- Building AI (ANN/CNN) architecture using Matlab
- We will able to know, what’s the term “Training” means in Artificial Intelligence
- About requirements that can influence the AI training process:
- Data
- Training data
- Validation data
- Testing data
- Hardware requirements to train network
- How to recognize the speech using AI
- How to extend our work to another real time applications
- Project development Skills:
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
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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.
