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Semi-Supervised Speech Confidence Detection using Pseudo-Labelling and Whisper Embeddings

2026-06-15 10:06:50
Adam Wynn, Jingyun Wang, Xiangyu Tan
arXiv_SD
         
Abstract

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 representations and achieve an overall accuracy of 75%. This study contributes to advancing speech analysis, enabling applications that support personalised learning and speaking skill development.

Abstract (translated)

URL

https://arxiv.org/abs/2606.16505

PDF

https://arxiv.org/pdf/2606.16505.pdf

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