AURA Score: A Metric For Holistic Audio Question Answering Evaluation
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| Format: | Preprint |
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2025
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| author | Dixit, Satvik Deshmukh, Soham Raj, Bhiksha |
| author_facet | Dixit, Satvik Deshmukh, Soham Raj, Bhiksha |
| contents | Audio Question Answering (AQA) is a key task for evaluating Audio-Language Models (ALMs), yet assessing open-ended responses remains challenging. Existing metrics used for AQA such as BLEU, METEOR and BERTScore, mostly adapted from NLP and audio captioning, rely on surface similarity and fail to account for question context, reasoning, and partial correctness. To address the gap in literature, we make three contributions in this work. First, we introduce AQEval to enable systematic benchmarking of AQA metrics. It is the first benchmark of its kind, consisting of 10k model responses annotated by multiple humans for their correctness and relevance. Second, we conduct a comprehensive analysis of existing AQA metrics on AQEval, highlighting weak correlation with human judgment, especially for longer answers. Third, we propose a new metric - AURA score, to better evaluate open-ended model responses. On AQEval, AURA achieves state-of-the-art correlation with human ratings, significantly outperforming all baselines. Through this work, we aim to highlight the limitations of current AQA evaluation methods and motivate better metrics. We release both the AQEval benchmark and the AURA metric to support future research in holistic AQA evaluation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_04934 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AURA Score: A Metric For Holistic Audio Question Answering Evaluation Dixit, Satvik Deshmukh, Soham Raj, Bhiksha Audio and Speech Processing Artificial Intelligence Audio Question Answering (AQA) is a key task for evaluating Audio-Language Models (ALMs), yet assessing open-ended responses remains challenging. Existing metrics used for AQA such as BLEU, METEOR and BERTScore, mostly adapted from NLP and audio captioning, rely on surface similarity and fail to account for question context, reasoning, and partial correctness. To address the gap in literature, we make three contributions in this work. First, we introduce AQEval to enable systematic benchmarking of AQA metrics. It is the first benchmark of its kind, consisting of 10k model responses annotated by multiple humans for their correctness and relevance. Second, we conduct a comprehensive analysis of existing AQA metrics on AQEval, highlighting weak correlation with human judgment, especially for longer answers. Third, we propose a new metric - AURA score, to better evaluate open-ended model responses. On AQEval, AURA achieves state-of-the-art correlation with human ratings, significantly outperforming all baselines. Through this work, we aim to highlight the limitations of current AQA evaluation methods and motivate better metrics. We release both the AQEval benchmark and the AURA metric to support future research in holistic AQA evaluation. |
| title | AURA Score: A Metric For Holistic Audio Question Answering Evaluation |
| topic | Audio and Speech Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2510.04934 |