Predicting the emotions based on emoji's and speech using machine learning techniques
Author(s):
Chamallamudi Lalitha Devi, M Divya sree , P.nagulshareef
Keywords:
Feature-extraction, Emoji’s, Decision Tree, MLP-Classifier, CNN model, Augmentation methods.
Abstract
Speech consists of assorted information, like language, emotions, what type of message to be communicated with others etc. Emotions are the part of human life in every situation, sometimes one get angry, sad, happy based on the dialogues and behavior of the opposite person. In this work, we have a tendency to square measure aiming to predict the emotions supported the audio files. At first the dataset encompass audio files. Here the emotions typically represented as happy, sad, surprised, angry etc., and could be divided into 2 varieties like positive emotions and negative emotions. Here emoji’s are used to predict the emotion of the person, so that it can be quickly identified, for every feeling there’ll be a revered emoji format supported that we have a tendency to square measure able to get emoji’s for the required emotions given within the datasets. Before applying ways or models on the dataset, feature extraction plays a big role during this speech feeling prediction. Afterward we have a tendency to square measure applying Machine Learning Techniques such as Decision Tree, MLP classifier, neural networks and Augmenting the information using noise injection with Laplace and logistic distribution and pitch shifting and trimming the data so as to induce sensible performance.
Article Details
Unique Paper ID: 153250

Publication Volume & Issue: Volume 8, Issue 6

Page(s): 552 - 557
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Latest Publication

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers