ADTA 5940: CAPSTONE OCT-09-2022 Venkata Sai Palivela, Manideep Janjala, Vamsi Krishna Reddy Gujjula Capstone_Group1

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CONCEPT PAPER

Speech Emotion Recognition

Introduction:

As humans, we tend to express our emotions or feelings through facial expressions or through

sign language or through speaking, here we are basing our concept paper on speech emotion

recognition, and we may know what pitch or tone of the words can represent certain emotions

figuratively. With more and more AI models coming in to picture these days, The usage and

implementation of these models simultaneously arouse. With this type of classification and

predictions, it can pave a path for upcoming models to be more focused on recognizing the

emotions through voice command, Using AI has become a daily routine beginning from the

morning to evening we will be using AI for calling, texting, navigation, news, playing songs, playing

updates, reading out an article and much more. With this much of advancement tables have been

turned around in recognizing the emotions of the user, as this can help the users better in

understanding their daily needs, understanding the tone of the speaker, emotions and can better

interact with them in gaining thorough insights and make significant changes accordingly, by

playing songs relating to situation or playing videos based on the current mood or showing

relevant places nearby and much more, this is being a new start from past years and we are

basing this as our theme to our final capstone project.

ADTA 5940: CAPSTONE OCT-09-2022 Venkata Sai Palivela, Manideep Janjala, Vamsi Krishna Reddy Gujjula Capstone_Group1

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Initial Analysis of the Dataset:

Here we are using audio dataset which is retrieved from “TORONTO EMOTION SPEECH SET

(TESS)” collection which is available at

https://tspace.library.utoronto.ca/handle/1807/24487

The data was created in 2010 by University of Toronto, Psychology Department, and the authors

include: Kate Dupuis, M. Kathleen Pichora-Fuller and this collection of datasets is published under

Creative Commons license Attribution-NonCommercial-NoDerivatives 4.0 International.

This dataset is capturing 7 emotions in audio files as a part of dataset which includes: Happy, Sad,

Fear, Angry, Disgust, Pleasant Surprise and Neutral, there are total 200 target words in the

dataset relating to these 7 emotions which are spoken by two actresses aged 26 and 64 and there

are 2800 stimuli (Audio Files) in total.

Objective:

Our project is based on a classification model which can help us analyze the emotion of speech

through the acquired audio files and predict the state of emotion respectively according to the

7 main emotions as we discussed above (Happy, Sad, Fear, Angry, Disgust, Pleasant Surprise

and Neutral).

Our objective of this project is to predict the state of emotions through the voice signatures,

audio files, and identifying human emotions based on the respective gender.

ADTA 5940: CAPSTONE OCT-09-2022 Venkata Sai Palivela, Manideep Janjala, Vamsi Krishna Reddy Gujjula Capstone_Group1

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Work Plan:

Firstly, we will be starting with exploration of data, also find speech signature for each kind of

emotion and will try to compare both (Speech Signature, Different kind of emotions) to monitor

any significant difference and then we will be extracting features from the audio files which we

can categorize them as training data for our model and thereafter we will test and validate our

model to check for accuracy. Then we will finally use the model to predict the state of emotion

with training and testing data for both respective genders voice.

Here we are planning to perform classification methods by using Neural Networks to gain

better insights from the dataset.

The libraries which we are going to use for the classification and prediction includes ‘librosa,

‘numpy’, ‘matplotlib’, ‘pandas’, ‘tensorflow’, ‘scikit learn’ which is used for audio analysis and

in turn help us in visualizing audio files and for the feature extraction we will be using Keras

framework to work with Neural Networks, and we are going to use speech recognition module

using python which can better help us in taking voice as an input for the model and help in final

testing for the model.

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