Mobile SER with DistilHuBERT Efficient, Accurate, Cross-Corpus Validated

📝 Original Paper Info

- Title: Distilled HuBERT for Mobile Speech Emotion Recognition A Cross-Corpus Validation Study
- ArXiv ID: 2512.23435
- Date: 2025-12-29
- Authors: Saifelden M. Ismail

📝 Abstract

Speech Emotion Recognition (SER) has significant potential for mobile applications, yet deployment remains constrained by the computational demands of state-of-the-art transformer architectures. This paper presents a mobile-efficient SER system based on DistilHuBERT, a distilled and 8-bit quantized transformer that achieves approximately 92% parameter reduction compared to full-scale Wav2Vec 2.0 models while maintaining competitive accuracy. We conduct a rigorous 5-fold Leave-One-Session-Out (LOSO) cross-validation on the IEMOCAP dataset to ensure speaker independence, augmented with cross-corpus training on CREMA-D to enhance generalization. Cross-corpus training with CREMA-D yields a 1.2% improvement in Weighted Accuracy, a 1.4% gain in Macro F1-score, and a 32% reduction in cross-fold variance, with the Neutral class showing the most substantial benefit at 5.4% F1-score improvement. Our approach achieves an Unweighted Accuracy of 61.4% with a quantized model footprint of only 23 MB, representing approximately 91% of the Unweighted Accuracy of a full-scale baseline. Cross-corpus evaluation on RAVDESS reveals that the theatrical nature of acted emotions causes predictions to cluster by arousal level rather than by specific emotion categories - happiness predictions systematically bleed into anger predictions, and sadness predictions bleed into neutral predictions, due to acoustic saturation when actors prioritize clarity over subtlety. Despite this theatricality effect reducing overall RAVDESS accuracy to 46.64%, the model maintains robust arousal detection with 99% recall for anger, 55% recall for neutral, and 27% recall for sadness. These findings demonstrate a Pareto-optimal tradeoff between model size and accuracy, enabling practical affect recognition on resource-constrained mobile devices.

💡 Summary & Analysis

- **Contribution 1:** Use of deep learning for enhancing image resolution and clarity - **Contribution 2:** Data augmentation strategies to strengthen the model and improve generalization performance - **Contribution 3:** A step-by-step methodology leading to effective output images

Simple Explanation and Metaphors: This research introduces a new way to improve photo quality by using deep learning, particularly CNNs. Think of it like moving from simple camera filters to an AI that learns and applies its own filters for sharper photos.

Sci-Tube Style Script:

1. Beginner: “How does this study use deep learning to make images clearer?”
2. Intermediate: “What role do CNNs play in enhancing image quality, especially with data augmentation?”
3. Advanced: “Can you explain the preprocessing, training, and post-processing steps used in this methodology?”

📄 Full Paper Content (ArXiv Source)

📊 논문 시각자료 (Figures)

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