Comments (3)
The VAD model is only fine-tuned on the MSP-Podcast dataset, which has several shortcomings for a full blown VAD model:
- Podcast recordings most likely do not contain all possible emotions, e.g. fear
- The dominance and arousal annotations show a high correlation, that is mimicked by the model, which means we most likely do not cover the 3D space of VAD in a meaningful way
Having this in mind I would propose to be very carefully when trying to map the VAD values to emotional categories.
Another way might be to further fine-tune the model on a given database containing the desired emotional categories, or using the embeddings of the model to train a simple classifier on such a database like we do in the notebook under the "https://github.com/audeering/w2v2-how-to/blob/main/notebook.ipynb" section.
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Thanks a million for the clarifications.
In general, the conversion from VAD to Ekman seems to provide useful results:
https://github.com/mirix/approaches-to-diarisation/tree/main/emotions
However, it is true that fear is never detected.
I will see what other models are available and pay more attention to which datasets were used.
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Hi @hagenw
I have forked MOSEI for SER:
https://huggingface.co/datasets/mirix/messaih
https://github.com/mirix/messaih
Now I will try to train a model and test it in a real-life scenario.
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Related Issues (14)
- Other pretrained models
- Error in using audinterface.Feature HOT 5
- How to fine-tune the pretrain-model HOT 3
- Fine tune on another dataset
- there is a error when running the note code HOT 1
- Range value of arousal, valence, dominance HOT 1
- Use emodb train and test splits
- audonnx requirements trainer and onnx depends on protobuf different protobuf HOT 1
- How to get both dimensional scores and embedding in one run? HOT 3
- Negative values for Arousal HOT 3
- URLError: <urlopen error [Errno 11004] getaddrinfo failed> HOT 4
- korean, depression/normal audio data set HOT 3
- Avoid random output in notebook
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