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.
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Hi,I had read your problem statement.I can complete the project within the [login to view URL] free to contact.I had completed music recommendation using face expression.