Cross-lingual vs. monolingual model performance in accented speech recognition

Utilizing Self-Supervised Learning (SSL) models for Speech Emotion Recognition (SER) has proven effective, yet limited research has explored cross-lingual scenarios. This study presents a comparative analysis between human performance and SSL models, beginning with a layer-wise analysis and an exploration of parameter-efficient fine-tuning strategies in monolingual, cross-lingual, and transfer learning contexts. We further compare the SER ability of models and humans at both utterance- and segment-levels. Additionally, we investigate the impact of dialect on cross-lingual SER through human eva

Research goal: What is the comparative performance of cross-lingual transfer from English self-supervised models versus monolingual Flemish models on accented speech recognition tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.