EmoHRNet: High-Resolution Emotion Recognition

• EmoHRNet is a high-resolution neural framework that integrates audio-visual and physiological modalities to achieve robust emotion recognition.
• It leverages hybrid fusion, hierarchical hypercomplex layers, and HRNet adaptations to preserve detailed temporal and spectral information.
• Empirical results show improvements in accuracy and F1 metrics, affirming its potential for real-time, multimodal affective computing applications.

EmoHRNet refers to a class of neural network architectures and system designs targeting high-fidelity emotion recognition in challenging, multimodal environments. The term arises across several research lines, including hybrid audio-visual fusion for “in the wild” facial and speech emotion recognition (Guo et al., 2020), hierarchical hypercomplex networks for processing physiological signals (Lopez et al., 13 Sep 2024), and, most recently, as a specific adaptation of High-Resolution Network architecture (HRNet) for speech emotion recognition (Muppidi et al., 7 Oct 2025). The following sections synthesize key technical principles, architectural specifics, and reported empirical results underlying EmoHRNet.

1. Technical Foundations and Architectural Paradigms

EmoHRNet implementations encompass three principal paradigms:

1. Hybrid Multimodal Fusion (Guo et al., 2020): Combines deep CNN-RNN branches for facial image analysis with diverse audio-based models, including SVMs trained on holistic acoustic features, LSTMs trained on windowed short-term audio features, and Inception(v2)-LSTM modules operating on matrix-formatted audio sequences. These are integrated by decision-level fusion for robust “in the wild” emotion prediction.
2. Hierarchical Hypercomplex Networks (Lopez et al., 13 Sep 2024): Utilize parameterized hypercomplex convolutions (PHCs) and multiplications (PHMs) for encoding intra-modal and inter-modal signal relationships. The design leverages Kronecker product algebraic structures to compress parameters and simultaneously capture correlations in EEG, ECG, GSR, and eye-tracking data.
3. High-Resolution Network Adaptation (HRNet for SER) (Muppidi et al., 7 Oct 2025): Applies HRNet’s paradigm of maintaining high-resolution feature representations—via parallel multiscale processing stages and fusion layers—to speech emotion recognition. The network processes audio samples transformed into Mel spectrograms and preserves fine temporal-spectral details for improved emotion classification accuracy.

2. Data Processing and Feature Extraction

Audio-Visual Hybrid Network (Guo et al., 2020)

• Facial images: Frames sampled at 60 fps; faces detected and aligned using facial landmarks, then normalized by affine transform prior to VGG-FACE processing.
• Audio features:
  • Holistic: 1582-dimensional openSMILE vector including MFCCs, chroma vector, spectral statistics, ZCR, mean/std.
  • Short-term: Segments of 100 ms with 50% overlap; each segment forms a 34-dimensional vector processed by LSTM or arranged as 34×n matrix for CNN-LSTM modeling.
  • Sequence count $n = (m - 50)/50$ for input of length $m$.

HRNet Speech Adaptation (Muppidi et al., 7 Oct 2025)

• Audio signals: Converted to Mel spectrograms via STFT.
• Augmentation: SpecAugment techniques—frequency masking ($f \sim U(0,F)$), time masking ($t \sim U(0,T)$).
• Input: High-resolution spectrogram images fed into parallel convolution streams.

Hypercomplex-EEG (Lopez et al., 13 Sep 2024)

• Modalities: EEG, ECG, GSR, eye tracking.
• Encoders: Input channel count $n$ determines hypercomplex parameterization; modality-specific embedders model intra-channel correlations, fusion module combines modalities with hypercomplex multiplication.

3. Fusion and Learning Strategies

• Multimodal Fusion (Hybrid network): Decision scores from VGG-LSTM facial CNNs, SVM, LSTM, and Inception(v2)-LSTM are aggregated with fusion weights determined by grid search.
• Hierarchical Fusion (Hypercomplex): PHC layers model intra-modal dependencies; PHM layers integrate modal embeddings for global emotion inference.
• Multi-Resolution Fusion (HRNet): Feature maps from branches operating at varied resolutions are combined via $1 \times 1$ convolution in the Fuse Layer, followed by global average pooling and a dense classification head.

4. Mathematical Formulation and Parameterization

• VGG-FACE layer initialization (Guo et al., 2020): $w \sim \mathcal{N}(0, 1 \times 10^{-4})$
• Hypercomplex weight decomposition (Lopez et al., 13 Sep 2024): $W = \sum_{i=1}^n A_i \otimes F_i$, with $A_i$ as algebraic matrices and $F_i$ as learnable parameters; reduces parameter count by factor $1/n$.
• Pooling and softmax in HRNet (Muppidi et al., 7 Oct 2025):

$$z_i = \frac{1}{H \times W} \sum_{h=1}^H \sum_{v=1}^W F_{FL, i, h, v}$$

$y_i = \frac{e^{z_i}}{\sum_{j=1}^C e^{z_j}}$

5. Empirical Results and Performance

System / Dataset	Accuracy / F1	Notable Metric	Reference
EmoHRNet Hybrid (AFEW, EmotiW)	55.61% val, 51.15% test	Unweighted acc.	(Guo et al., 2020)
EmoHRNet HRNet Speech (RAVDESS)	92.45%	Accuracy	(Muppidi et al., 7 Oct 2025)
EmoHRNet HRNet Speech (IEMOCAP)	80.06%	Accuracy	(Muppidi et al., 7 Oct 2025)
EmoHRNet HRNet Speech (EMOVO)	92.77%	Accuracy	(Muppidi et al., 7 Oct 2025)
Hierarchical Hypercomplex (MAHNOB-HCI EEG)	F1: 0.557 (arousal), 0.685 (valence)	Relative gain	(Lopez et al., 13 Sep 2024)

The hybrid network outperformed visual-only and audio-only baselines (e.g., 38.81% baseline on EmotiW). The HRNet adaptation for speech achieved higher accuracy than existing attention-based and fused CNN models. Hypercomplex networks reported significant F1 improvements over prior multimodal fusion approaches for physiological data.

6. Contributions and Innovations

• HRNet for SER: First use of high-resolution networks in speech emotion recognition, enabling preservation of fine acoustic and temporal details through deep multiscale architectures (Muppidi et al., 7 Oct 2025).
• Hypercomplex parameterization: Explicit modeling of intra- and inter-modal correlations via algebraic layer construction, achieving parameter efficiency and improved feature discrimination (Lopez et al., 13 Sep 2024).
• Hybrid audio-visual fusion: Integration of supervised SVM, LSTM, CNN-LSTM, and fusion weighting for robust multimodal inference “in the wild” (Guo et al., 2020).

7. Applications, Limitations, and Future Directions

Applications:

• Empathetic human-machine interfaces in virtual assistants and robotics
• Real-time sentiment profiling in customer service and healthcare diagnostics
• Robust multimodal emotion monitoring using physiological inputs in clinical research

Limitations & Future Work:

• Enhanced robustness to environmental noise, occlusions, and cross-domain variability highlighted as ongoing challenges.
• Future directions include integrating attention mechanisms for deeper audio modeling, learnable fusion strategies beyond grid search (e.g., multi-modal transformers), joint end-to-end optimization across modalities, and extending hypercomplex approaches to additional data domains (Guo et al., 2020, Lopez et al., 13 Sep 2024, Muppidi et al., 7 Oct 2025).
• Prospects also include investigating advanced regularization (dropout, data augmentation), larger and more varied training cohorts, and real-world system deployments.

EmoHRNet thus comprises a technically diverse set of architectures unified by the principle of high-resolution, cross-modal, robust emotion representation. The synergies between parallel feature preservation, algebraic parameterization, and fusion methodologies yield substantial gains across a spectrum of affective computing tasks.