Deep learning for Speech Recognition Recogniztion
Budget: €30 – €250 EUR
For this project there should be a major emphasis and focus on utilizing deep learning techniques using Python and Jupyter notebook for visualizations (.ypnb files). Autoencoding.
Each utterance in a dialogue has been labelled by any of these seven emotions: Neutral, Joyful, Peaceful, Powerful, Scared, Mad and Sad.
Question to answer: How can be improved detection, classification and accuracy of speech recognition?
Datasets and reference source of similar project
Data and Python code source: https://github.com/declare-lab/MELD
Titles references: MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations:
https://arxiv.org/pdf/1810.02508.pdf
Models
Deep learning technique: recurrent neural network (RNN) for Automatic speech recognition (audio and text files).
Deep learning model: Convolutional neural network (CNN)
Each utterance in a dialogue has been labelled by any of these seven emotions: Neutral, Joyful, Peaceful, Powerful, Scared, Mad and Sad.
Question to answer: How can be improved detection, classification and accuracy of speech recognition?
Datasets and reference source of similar project
Data and Python code source: https://github.com/declare-lab/MELD
Titles references: MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations:
https://arxiv.org/pdf/1810.02508.pdf
Models
Deep learning technique: recurrent neural network (RNN) for Automatic speech recognition (audio and text files).
Deep learning model: Convolutional neural network (CNN)