Emotion detection from hindi speech

I want to develop code to do emotion detection on speech by training a model by pre-processes the test voice signal, extract selective MFCC, LPC, pitch, and voice quality features,

then classify the speech using Catboost.

Step 1. Read each file with different sentiments and languages

Step 2. Filter the voices. We removed blanks

Step 3. Extract LPC and MFCC features. Calculate Pitch. Club them all as features

Step 4. Features satisfying a threshold are retained

Step 5. Assign labels to features as per the sentiment in the voice

Step 6. Design a Catboost model having relevant parameters

Step 7. Using features and labels train the model

Step 8. To predict the sentiment

Take a new voice for output

Repeat steps 2 through 4

Run the model with the new features to predict the sentiment

We used Catboost to extract the categorical features and to predict the sentiments from voices.

A detailed description is there in the attached research paper.

All these algortithms need to be implemented.

Datasets to be prepared using dialogues from movies.

Skills: Deep Learning, Machine Learning (ML), Algorithm, Python

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