A Semi-Supervised Framework for Speech Confidence Detection using Whisper
Abstract
Automatic detection of speaker confidence is critical for adaptive computing but remains constrained by limited labelled data and the subjectivity of paralinguistic annotations. This paper proposes a semi-supervised hybrid framework that fuses deep semantic embeddings from the Whisper encoder with an interpretable acoustic feature vector composed of eGeMAPS descriptors and auxiliary probability estimates of vocal stress and disfluency. To mitigate reliance on scarce ground truth data, we introduce an Uncertainty-Aware Pseudo-Labelling strategy where a model generates labels for unlabelled data, retaining only high-quality samples for training. Experimental results demonstrate that the proposed approach achieves a Macro-F1 score of 0.751, outperforming self-supervised baselines, including WavLM, HuBERT, and Wav2Vec 2.0. The hybrid architecture also surpasses the unimodal Whisper baseline, yielding a 3\% improvement in the minority class, confirming that explicit prosodic and auxiliary features provide necessary corrective signals which are otherwise lost in deep semantic representations. Ablation studies further show that a curated set of high confidence pseudo-labels outperforms indiscriminate large scale augmentation, confirming that data quality outweighs quantity for perceived confidence detection.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel semi-supervised framework for speech confidence detection that integrates deep learning with interpretable acoustic features, significantly advancing the field of affective computing. The methodology is innovative, addressing key challenges in data scarcity and subjective annotation, while the experimental results demonstrate strong performance and robustness.
Methodology
The paper introduces a semi-supervised hybrid framework for speech confidence detection that effectively combines deep semantic embeddings from the Whisper model with interpretable acoustic features. The methodology is robust, employing an Uncertainty-Aware Pseudo-Labelling strategy that prioritizes high-quality pseudo-labels over indiscriminate augmentation, which is a significant advancement in dealing with limited labelled data. The integration of eGeMAPS descriptors and auxiliary features for vocal stress and disfluency detection enhances the model's ability to capture nuanced acoustic signals associated with speaker confidence. The late fusion strategy used to combine different modalities is well-justified and effectively addresses the limitations of relying solely on deep semantic representations.
Results & evaluation
The experimental evaluation is thorough, employing a well-structured 5-fold cross-validation approach to ensure the reliability of results. The paper reports a Macro-F1 score of 0.751, which is a notable improvement over various self-supervised baselines, demonstrating the effectiveness of the proposed method. The ablation studies provide insights into the importance of data quality and the contributions of different components of the model, reinforcing the claims made about the hybrid architecture's advantages. However, the paper could benefit from additional qualitative evaluations or user studies to further validate the model's performance in real-world scenarios.
Reproducibility
The paper provides a detailed description of the methodology, including dataset creation, feature extraction, and model architecture, which aids in reproducibility. However, the absence of publicly available datasets and code repositories limits the ability of other researchers to replicate the study fully. The authors should consider releasing their code and datasets to enhance transparency and facilitate further research.
Limitations
The primary limitation of the study is the reliance on a relatively small ground truth dataset (N=600), which may affect the generalizability of the findings. Additionally, the model's performance may be constrained by the subjective nature of confidence detection and the cultural variability in expressing confidence. The focus on short audio clips also limits the model's ability to capture context and fluctuations in confidence over longer interactions.
Broader impact
The proposed framework has significant implications for applications in affective computing, particularly in enhancing human-computer interaction and adaptive learning environments. By enabling machines to detect speaker confidence, the framework could improve the responsiveness of virtual assistants and educational tools, fostering more engaging and supportive user experiences. Furthermore, the insights gained from this research could contribute to mental health monitoring and interventions by identifying low confidence as a precursor to anxiety. The paper presents a novel semi-supervised framework for speech confidence detection that integrates deep learning with interpretable acoustic features, significantly advancing the field of affective computing. The methodology is innovative, addressing key challenges in data scarcity and subjective annotation, while the experimental results demonstrate strong performance and robustness.
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