emotion-recognition-wav2vec2-IEMOCAP
Maintainer: speechbrain
94
🛠️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The emotion-recognition-wav2vec2-IEMOCAP model is a speech emotion recognition system developed by SpeechBrain. It uses a fine-tuned wav2vec2 model to classify audio recordings into one of several emotional categories. This model is similar to other speech emotion recognition models like wav2vec2-lg-xlsr-en-speech-emotion-recognition and wav2vec2-large-robust-12-ft-emotion-msp-dim, which also leverage the wav2vec2 architecture for this task.
Model inputs and outputs
Inputs
- Audio recordings: The model takes raw audio recordings as input, which are automatically normalized to 16kHz single-channel format if needed.
Outputs
- Emotion classification: The model outputs a predicted emotion category, such as "angry", "calm", "disgust", etc.
- Confidence score: The model also returns a confidence score for the predicted emotion.
Capabilities
The emotion-recognition-wav2vec2-IEMOCAP model can accurately classify the emotional content of audio recordings, achieving 78.7% accuracy on the IEMOCAP test set. This makes it a useful tool for applications that require understanding the emotional state of speakers, such as customer service, mental health monitoring, or interactive voice assistants.
What can I use it for?
This model could be integrated into a variety of applications that need to analyze the emotional tone of speech, such as:
- Call center analytics: Analyze customer service calls to better understand customer sentiment and identify areas for improvement.
- Mental health monitoring: Use the model to track changes in a patient's emotional state over time as part of remote mental health monitoring.
- Conversational AI: Incorporate the model into a virtual assistant to enable more natural and empathetic interactions.
Things to try
One interesting thing to try with this model is to experiment with different audio preprocessing techniques, such as data augmentation or feature engineering, to see if you can further improve its performance on your specific use case. You could also explore combining this model with other speech technologies, like speaker verification, to create more advanced speech analysis systems.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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