praweshd/speech_emotion_recognition
In this project, the performance of speech emotion recognition is compared between two methods (SVM vs Bi-LSTM RNN).Conventional classifiers that uses machine learning algorithms has been used for decades in recognizing emotions from speech. However, in recent years, deep learning methods have taken the center stage and have gained popularity for their ability to perform well without any input hand-crafted features. Speech emotion on sets obtained from RAVDESS corpus is classified using a conventionally used Support Vector Machine (SVM) and its performance is compared to that of a bidirectional long short-term memory (LSTM).
This project helps researchers and developers understand how different machine learning approaches perform in recognizing emotions from speech. It takes audio recordings (specifically from the RAVDESS corpus) and outputs classifications of the emotions expressed, comparing the effectiveness of traditional Support Vector Machines (SVM) against modern deep learning Bidirectional LSTMs.
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Use this if you are exploring or developing systems that need to automatically detect emotions from spoken language and want to compare different methodological approaches.
Not ideal if you need a production-ready, highly accurate emotion recognition system for general use cases, as this project focuses on a comparative analysis of methods.
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Jan 02, 2024
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