# PAM: Prompting Audio-Language Models for Audio Quality Assessment

arXiv: https://arxiv.org/abs/2402.00282 · HTML: https://mlsanity.com/paper/2402.00282/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2024.

## Built on by (most cited first, 20 of 20)

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- [From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modeling](https://mlsanity.com/paper/2510.00743/) (2025)
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- [TuneJury: An Open Metric for Improving Music Generation Preference Alignment](https://mlsanity.com/paper/2606.17006/) (2026)
- [AnyAudio-Judge: A Dynamic Rubric-Based Benchmark and Evaluator for Audio Instruction Following](https://mlsanity.com/paper/2606.03116/) (2026)
- [Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing](https://mlsanity.com/paper/2602.22659/) (2026)
- [DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer](https://mlsanity.com/paper/2508.13786/) (2025)
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- [Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings](https://mlsanity.com/paper/2607.03806/) (2026)
- [MMMOS: Multi-domain Multi-axis Audio Quality Assessment](https://mlsanity.com/paper/2507.04094/) (2025)
- [Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations](https://mlsanity.com/paper/2603.16201/) (2026)
- [CAF-Score: Calibrating CLAP with LALMs for Reference-free Audio Captioning Evaluation](https://mlsanity.com/paper/2603.19615/) (2026)
- [MuseCritic: Learning Multi-Aspect Song Rewards through Natural-Language Aesthetic Critiques](https://mlsanity.com/paper/2608.11755/) (2026)

## Abstract (arXiv)

While audio quality is a key performance metric for various audio processing tasks, including generative modeling, its objective measurement remains a challenge. Audio-Language Models (ALMs) are pre-trained on audio-text pairs that may contain information about audio quality, the presence of artifacts, or noise. Given an audio input and a text prompt related to quality, an ALM can be used to calculate a similarity score between the two. Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks. Contrary to other "reference-free" metrics, PAM does not require computing embeddings on a reference dataset nor training a task-specific model on a costly set of human listening scores. We extensively evaluate the reliability of PAM against established metrics and human listening scores on four tasks: text-to-audio (TTA), text-to-music generation (TTM), text-to-speech (TTS), and deep noise suppression (DNS). We perform multiple ablation studies with controlled distortions, in-the-wild setups, and prompt choices. Our evaluation shows that PAM correlates well with existing metrics and human listening scores. These results demonstrate the potential of ALMs for computing a general-purpose audio quality metric.