audio_meld_seac_finetuned

Audio Emotion Recognition (MELD → SEAC, Audio-only)

Overview

This model performs speech emotion recognition from audio only.

It uses a pretrained Wav2Vec2 encoder (frozen) as a feature extractor, followed by a lightweight classification head.

The model was:

• Pretrained on: MELD (English conversational emotions)
• Fine-tuned on: SEAC (Serbian emotional speech)
• Task: 5-class emotion classification from speech audio

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Emotions

The model predicts:

• neutral
• joy
• anger
• sadness
• fear

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Architecture

• Encoder: facebook/wav2vec2-base (frozen)
• Pooling: Mean pooling over temporal hidden states
• Classifier: Fully connected classification head
• Training strategy: Transfer learning (classifier-only fine-tuning)

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Transfer Learning Setup

Stage 1 – Pretraining (MELD)

• Audio-only emotion classification

Stage 2 – Fine-tuning (SEAC)

• Encoder frozen
• Only classification head updated

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Evaluation (SEAC Test Set)

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Notes

• Sampling rate: 16 kHz
• Mean temporal pooling is used to obtain utterance-level embeddings.
• The released weights include only the classification head. The encoder is loaded from facebook/wav2vec2-base.

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