Dalia-Sher/Speech-Emotion-Recognition-using-BLSTM-with-Attention
We present a study of a neural network based method for speech emotion recognition, using audio-only features. In the studied scheme, the acoustic features are extracted from the audio utterances and fed to a neural network that consists of CNN layers, BLSTM combined with an attention mechanism layer, and a fully-connected layer. To illustrate and analyze the classification capabilities of the network we used the t-SNE method. We evaluated our model using RAVDESS and IEMOCAP databases.
This project helps researchers and engineers analyze and classify emotions expressed in spoken audio. It takes raw audio recordings as input and outputs classifications of the emotions present, such as happiness, sadness, or anger. This is useful for anyone studying human affect, designing empathetic AI, or analyzing communication patterns.
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Use this if you need to automatically identify and categorize emotions from spoken language in audio files.
Not ideal if you need to analyze emotions from text, images, or video, as this project focuses exclusively on audio-only features.
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Jul 24, 2024
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