Detecting False Alarms and Misses in Audio Captions

TL;DR

This paper introduces an automatic metric that identifies specific errors like misses and false alarms in audio captions, providing detailed feedback to improve caption quality.

Contribution

The work presents a novel metric that automatically detects shortcomings in audio captions, including misses and false alarms, with detailed error analysis metrics.

Findings

The metric effectively identifies errors in audio captions.

It reports recall, precision, and F-score for error detection.

The approach aids in diagnosing and improving audio captioning models.

Abstract

Metrics to evaluate audio captions simply provide a score without much explanation regarding what may be wrong in case the score is low. Manual human intervention is needed to find any shortcomings of the caption. In this work, we introduce a metric which automatically identifies the shortcomings of an audio caption by detecting the misses and false alarms in a candidate caption with respect to a reference caption, and reports the recall, precision and F-score. Such a metric is very useful in profiling the deficiencies of an audio captioning model, which is a milestone towards improving the quality of audio captions.