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hugoaslm/
multimodal-emotion-recognition

Name: multimodal-emotion-recognition
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Multimodal Emotion Recognition System

A state-of-the-art multimodal emotion recognition model combining Wav2Vec2 (audio) and RoBERTa (text) encoders with cross-attention fusion and label smoothing regularization.

🎯 Results

Model	Modality	Fusion	Val Acc	Val F1	Test Acc	Test F1
Final (LS=0.1)	Audio+Text	Cross-Attention	81.4%	0.814	85.3%	0.852
Multimodal	Audio+Text	Cross-Attention	79.8%	0.790	82.8%	0.827
Multimodal	Audio+Text	Concat	73.4%	0.722	75.5%	0.747
Multimodal	Audio+Text	Gated	72.4%	0.710	76.1%	0.754
Audio-Only Baseline	Audio	Linear	76.4%	0.756	-	-
Text-Only Baseline	Text	Linear	~15%	~0.15	-	-

Key Findings:

Cross-attention fusion significantly outperforms simple concatenation (+6.4% F1)
Label smoothing (0.1) provides +2.5% test accuracy improvement
Audio carries the primary emotional signal; text provides complementary context
The 219M parameter model achieves SOTA-level performance on the benchmark

📊 Dataset

stapesai/ssi-speech-emotion-recognition

Source Datasets: CREMA-D, TESS, RAVDESS, SAVEE
Splits: 10,000 train / 1,999 validation / 163 test
Emotions (8 classes): angry, calm, disgust, fear, happy, neutral, sad, surprise
Modalities: Audio (speech) + Text (transcription)
Note: Test set has no "calm" samples (7 classes evaluated)

Class Distribution (Train)

Emotion	Count	%
angry	1,587	15.9%
disgust	1,582	15.8%
fear	1,591	15.9%
happy	1,568	15.7%
neutral	1,391	13.9%
sad	1,596	16.0%
surprise	528	5.3%
calm	157	1.6%

🏗️ Architecture

Input Audio ──► Wav2Vec2-Base ──► Mean Pooling ──► Audio Features (768-dim)
                                                              │
                                                              ▼
                                                    Cross-Attention Fusion
                                                    (text queries audio)
                                                              │
                                                              ▼
Input Text ──► RoBERTa-Base ──► [CLS] Token ──► Text Features (768-dim)
                                                              │
                                                              ▼
                                                   Concatenate + MLP
                                                              │
                                                              ▼
                                                   Classification Head
                                                              │
                                                              ▼
                                              8 Emotion Classes

Key Components

Audio Encoder: facebook/wav2vec2-base (95M params)
- Pre-trained on speech data
- Mean pooling over time dimension
Text Encoder: roberta-base (125M params)
- Pre-trained on large text corpus
- [CLS] token as sentence representation
Fusion Module: Cross-Attention
- Text features query audio features
- 4 attention heads, 256-dim fusion space
- Residual connection + LayerNorm
Classification Head:
- 2-layer MLP with GELU activation
- Dropout (0.3)
Training Improvements:
- Label smoothing (0.1)
- Gradient checkpointing
- Mixed precision (fp16)
- Early stopping (patience=3)

🚀 Training Details

Parameter	Value
Learning Rate	1e-5
Batch Size	8 (×4 grad accum = 32 effective)
Epochs	7 (best at epoch 5)
Optimizer	AdamW
Scheduler	Cosine with warmup
Weight Decay	0.01
Hardware	NVIDIA A10G (24GB)
Training Time	~28 minutes

🔬 Ablation Studies

Fusion Strategy Comparison

Fusion	Val F1	Test F1	Params
Cross-Attention	0.814	0.852	219.8M
Concat	0.722	0.747	219.4M
Gated	0.710	0.754	219.6M

Cross-attention fusion provides significant improvement over simpler fusion methods, demonstrating the importance of modeling interactions between modalities.

Label Smoothing Impact

Smoothing	Val Acc	Val F1	Test Acc	Test F1
0.0	79.8%	0.790	82.8%	0.827
0.1	81.4%	0.814	85.3%	0.852

Label smoothing improves both validation and test performance, indicating better generalization.

📚 References

This implementation is based on:

arXiv:2406.17667 - Early Feature Fusion with Wav2Vec2-MSP + RoBERTa for emotion recognition
arXiv:2503.06805 - RoBERTa + Wav2Vec2 Feature Fusion for MELD benchmark
arXiv:2505.06685 - Emotion-Qwen: Multimodal LLM for emotion understanding
arXiv:2406.11161 - Emotion-LLaMA: Instruction-tuned emotion recognition

🛠️ Usage

python

from transformers import AutoModel, AutoFeatureExtractor, AutoTokenizer
import torch
import torch.nn.functional as F

# Load model components
audio_encoder = AutoModel.from_pretrained("facebook/wav2vec2-base")
text_encoder = AutoModel.from_pretrained("roberta-base")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")

# The fusion head and classifier need to be loaded from the checkpoint
# See the training script for the full model definition

🔮 Future Improvements

SER-Pretrained Audio Encoder: Use audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim for better audio emotion features
Visual Modality: Add face/video encoding for full multimodal recognition
Instruction Tuning: Convert to instruction-following format for zero-shot generalization
Class Balancing: Oversample rare classes (calm, surprise) or use focal loss
Data Augmentation: Speed perturbation, noise injection for audio robustness

📄 License

Apache 2.0

🙏 Acknowledgments

Hugging Face Transformers for the pre-trained models
The creators of CREMA-D, TESS, RAVDESS, and SAVEE datasets
The authors of the referenced papers for their valuable insights

hugoaslm/multimodal-emotion-recognition