Semantic-Aware Confidence Calibration for Automated Audio Captioning
Abstract
Automated audio captioning models frequently produce overconfident predictions regardless of semantic accuracy, limiting their reliability in deployment. This deficiency stems from two factors: evaluation metrics based on n-gram overlap that fail to capture semantic correctness, and the absence of calibrated confidence estimation. We present a framework that addresses both limitations by integrating confidence prediction into audio captioning and redefining correctness through semantic similarity. Our approach augments a Whisper-based audio captioning model with a learned confidence prediction head that estimates uncertainty from decoder hidden states. We employ CLAP audio-text embeddings and sentence transformer similarities (FENSE) to define semantic correctness, enabling Expected Calibration Error (ECE) computation that reflects true caption quality rather than surface-level text overlap. Experiments on Clotho v2 demonstrate that confidence-guided beam search with semantic evaluation achieves dramatically improved calibration (CLAP-based ECE of 0.071) compared to greedy decoding baselines (ECE of 0.488), while simultaneously improving caption quality across standard metrics. Our results establish that semantic similarity provides a more meaningful foundation for confidence calibration in audio captioning than traditional n-gram metrics.
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 framework for confidence-calibrated audio captioning that redefines correctness through semantic similarity. The contributions are significant, as they advance the state of the art in audio captioning by addressing overconfidence and improving the reliability of model predictions through innovative methodologies.
Methodology
The paper introduces a novel framework for confidence calibration in automated audio captioning that integrates a learned confidence prediction head with a Whisper-based model. This approach is innovative as it shifts the focus from traditional n-gram overlap metrics to semantic similarity for evaluating correctness, which is a significant advancement in the field. The architecture is well-defined, with clear descriptions of the confidence prediction head, temperature scaling, and confidence-guided beam search. The methodology is robust and addresses existing limitations in audio captioning systems effectively.
Results & evaluation
The experiments conducted on the Clotho v2 dataset are comprehensive, demonstrating substantial improvements in both calibration and caption quality metrics. The results are compelling, with a dramatic reduction in Expected Calibration Error (ECE) from 0.488 to 0.071, showcasing the effectiveness of the proposed method. Additionally, the paper provides quantitative results across multiple evaluation metrics (BLEU, CIDEr, CLAP similarity), which strengthens the validity of the findings.
Reproducibility
The implementation details are adequately described, including the model architecture, training parameters, and evaluation metrics. However, the lack of a publicly available code repository or demo URL limits reproducibility. Future work should consider making the code accessible to facilitate validation of results by the research community.
Limitations
The paper acknowledges several limitations, including the somewhat arbitrary threshold for semantic correctness and the evaluation being limited to the Clotho dataset. The authors also note that the confidence head may not capture all sources of uncertainty, suggesting areas for future exploration. These limitations are important to consider for the generalization of the findings.
Broader impact
The proposed framework has significant implications for real-world applications of automated audio captioning, particularly in accessibility technologies and content indexing. By improving the reliability of predictions, this work could enhance user trust in automated systems, leading to broader adoption in various domains. The paper presents a framework for confidence-calibrated audio captioning that redefines correctness through semantic similarity. The contributions are significant, as they advance the state of the art in audio captioning by addressing overconfidence and improving the reliability of model predictions through innovative methodologies.
Author of this paper? Send a correction.