Gender-Aware Conformer-Transformer for Fair Speech Emotion Recognition in Sensor-Oriented Applications
Year: 2026
Type: article
Abstract: Notwithstanding the growing role of speech emotion recognition (SER) in human-computer interaction, deep-learning models often display a gender imbalance, recognizing female speech less accurately.We explore a gender-aware conformer-transformer framework to solve this, which first learns gender-related patterns under noisy conditions instead of undergoing the standard end-to-end training.These patterns are then fed into a transformer emotion decoder via cross attention.A conformer encoder was pretrained on gender classification under heavy noise with a robustness of 97.73%.This framework achieved a weighted accuracy of 88.64% and narrowed the gender gap (91.1% recognition accuracy for female speech versus 85.2% for males) on the Ryerson audio-visual database of emotional speech and song dataset.The emotion recognition dropped, as expected, on the crowd-sourced emotional multimodal actors dataset without adaptation, owing to domain differences.However, the gender classification was reasonably accurate (F1 = 0.386).We conclude that using explicit gender information improves fairness under matched conditions, although cross-dataset generalization remains challenging.
Author: Haiyun Ma
Institution: Nilai University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166674817
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