Speech Emotion Recognition Using Machine Learning

Project Code :TMMAAI200

Objective

Emotion of the recorded voice (Anger, Happy...) is recognized using machine learning algorithm

Abstract

Speech emotion recognition (SER) is a challenging issue because it is not clear which features are effective for classification. Emotionally related features are always extracted from speech signals for emotional classification. Different persons have different emotions and altogether a different way to express it. 

Speech emotion do have different energies, pitch variations are emphasized if considering different subjects. 

Therefore, the speech emotion recognition is a demanding task in computing vision. Handcrafted features are mainly used for emotional identification from audio signals. However, these features are not sufficient to correctly identify the emotional state of the speaker. 

Here, the speech emotion recognition and the classifiers are used to differentiate emotions such as surprise, anger, sadness, neutral state, happiness, etc. 

The advantages of a convolutional neural network (CNN) are investigated in the proposed work. A neural network framework is used to extract the features from speech emotion databases. In this work, we do the preprocessing for the input audio file and CNN layers adopt for feature selection (FS) approach to find the discriminative and most important features for SER. The proposed method shows the better results in terms of the emotion recognition.

 

Keywords: - Emotion recognition, Convolutional neural network, Features, Speech, Deep learning.

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 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 MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • 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.,
  • 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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