Semi-Supervised Speech Confidence Detection using Pseudo-Labelling and Whisper Embeddings
Understanding speaker confidence is crucial in educational settings, as it can enhance personalised feedback and improve learning outcomes. This study introduces a novel framework for detecting speaker confidence by integrating human-engineered features with embeddings from the Whisper encoder. To address data limitations, a pseudo-labelling technique is employed to expand the labelled dataset, allowing the model to learn from both human-annotated and model-generated labels. The framework combines traditional speech features including pitch, volume, rate of speech, and the presence of disfluencies and stress, with Whisper embeddings, and uses a co-attention mechanism to fuse these represe...
| Authors | Adam Wynn, Jingyun Wang, Xiangyu Tan |
|---|---|
| Primary category | cs.SD |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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