Robust Audio Large Models Achieved with RSA-Bench and Diverse Four-Scenario Testing

The reliability of Audio Large Models (ALMs) in realistic conditions remains a significant challenge, despite recent advances in their capabilities. Yibo Zhang, Liang Lin, and Kaiwen Luo, from Beijing University of Posts and Telecommunications and Nanyang Technological University, alongside Shilinlu Yan, Jin Wang, Yaoqi Guo et al., address this issue by introducing RSA-Bench, a new benchmark designed to rigorously test ALMs using simulated real-world soundscapes. This research moves beyond simplistic evaluations employing artificial noise, instead focusing on complex ‘acoustic ecology’ , the layered sounds of authentic physical environments , to assess model performance. The study reveals a critical gap between a model’s ability to recognise basic sounds and its capacity for higher-level reasoning when faced with realistic interference, highlighting the surprising detrimental effects of seemingly helpful speech enhancement techniques. Ultimately, this work provides crucial insights into the limitations of current ALMs and guides future development towards more robust and ecologically valid audio processing systems.

Realistic Acoustic Environments for Robust ALLM Evaluation

The limitations of current Automatic Language Modelling (ALLM) evaluations in real-world deployment are significant. Existing methods predominantly utilise synthetic Gaussian noise or simplistic single-source interference, which fail to accurately represent the complex, multi-layered acoustic dynamics present in authentic physical environments, a phenomenon termed “Acoustic Ecology”. To address this ecological deficit, researchers have developed RSA-Bench, a comprehensive robustness benchmark intended to rigorously test ALLMs using high-fidelity auditory scene simulations. This novel approach constructs evaluation samples by naturally superimposing diverse environmental soundscapes, encompassing Pasture, Extreme Weather, Classroom, and Outdoors, onto clean speech signals across a range of interference intensities.

The benchmark’s design moves beyond traditional methods by focusing on realistic acoustic conditions. RSA-Bench aims to provide a more ecologically valid assessment of ALLM performance, simulating the challenges encountered in genuine operational settings. By employing naturally recorded soundscapes, the benchmark introduces a greater degree of complexity and realism than previously available, allowing for a more nuanced understanding of ALLM robustness. This facilitates the identification of weaknesses and guides the development of more resilient speech recognition systems. A key contribution of this work is the creation of a benchmark that specifically targets the challenges posed by complex acoustic environments.

RSA-Bench offers a standardised platform for evaluating ALLMs under ecologically valid conditions, enabling direct comparison of different models and algorithms. The benchmark’s comprehensive nature, encompassing a variety of environmental soundscapes and interference levels, provides a thorough assessment of ALLM performance. This ultimately supports the development of ALLMs capable of functioning reliably in diverse and challenging real-world scenarios.

ASR Performance Under Multi-Source Acoustic Interference

The tables provided compare the performance of different speech recognition systems, specifically Step1 and Step2 from MERaLION, under increasing levels of multi-source acoustic interference in two scenarios: Pasture and Classroom. The interference is quantified by the number of interfering noise sources (K), which increases from 1 to 4. ### Key Findings: #### Step1: – MERaLION: – In both the Pasture and Classroom scenarios, as the number of interfering noise sources increases, all denoising methods show a significant improvement in ASR performance compared to the no-denoise baseline (Noise). – The PyRNNoise method generally performs well across different levels of interference. – DeepFilterNet also shows good performance but is slightly less effective than PyRNNoise. – In both scenarios, all denoising methods improve ASR performance compared to the no-denoise baseline (Noise). – The improvement in ASR for Step2 is generally higher than that of Step1. – DeepFilterNet shows a notable improvement in ASR and other metrics like ER, GR, MR, SQA, and SI. #### Step2: – MERaLION: – In the Pasture scenario, all denoising methods show significant improvements across various tasks (ER, GR, MR, SQA, SI) as the number of interfering noise sources increases. – The PyRNNoise method performs particularly well in improving these metrics. – DeepFilterNet also shows good performance but is slightly less effective than PyRNNoise. – In the Classroom scenario, all denoising methods show significant improvements across various tasks (ER, GR, MR, SQA, SI) as the number of interfering noise sources increases. – The DeepFilterNet method shows a notable improvement in ASR and other metrics like ER, GR, MR, SQA, and SI. ### Summary: – Denoising Methods generally improve performance across both Step1 and Step2 under increasing levels of multi-source acoustic interference. – DeepFilterNet performs well but is slightly less effective than PyRNNoise in some scenarios. – PyRNNoise shows the best overall performance, particularly in improving ASR and other metrics like ER, GR, MR, SQA, and SI. The research addresses a critical gap in existing evaluations, which typically rely on artificial noise or simplistic interference, failing to capture the complex acoustic dynamics of authentic physical spaces. This new benchmark constructs evaluation samples by naturally layering diverse soundscapes, including those representing Pasture, Extreme Weather, Classroom, and Outdoors, onto clean speech signals, varying the intensity of the interference. Experiments utilising the RSA-Bench dataset, comprising over 100,000 samples across six core tasks ranging from Automatic Speech Recognition (ASR) to complex audio-based reasoning, revealed a significant “Perception-Cognition Gap”.

Results demonstrate that while models maintain relative resilience in low-level recognition tasks, they experience a functional collapse in high-order reasoning when exposed to acoustic stress. The team measured a precipitous decline in capabilities as the acoustic environment became more complex, highlighting a widespread vulnerability across current ALLM architectures. This degradation was particularly pronounced in tasks demanding precise semantic reasoning. Further investigation uncovered a surprising “Denoising Paradox”. Tests prove that applying standard speech enhancement techniques often exacerbates performance degradation, as ALLMs exhibit a heightened sensitivity to the semantic distortions introduced by denoising artifacts rather than the natural background noise itself.

Scientists found that these algorithms frequently disrupt the integrity of the original audio, leading to performance that is not restored, but further diminished. The study also established that “vocal-like” interference, such as background laughter, proves significantly more destructive than simple noise, challenging the models’ auditory processing. The work confirms a universal performance decline across diverse interference types, demonstrating that high performance in clean environments does not translate to reliability in complex, real-world deployments. The study introduces RSA-Bench, a new benchmark utilising high-fidelity auditory scene simulations to rigorously test model performance across a range of tasks, from basic perception to complex reasoning. Findings reveal a notable disparity between perceptual capabilities and higher-order cognitive functions, with models exhibiting a functional collapse in reasoning tasks under acoustic stress. Furthermore, the research highlights the detrimental impact of certain types of interference, specifically vocal-like sounds, and the counterintuitive effect of standard speech enhancement techniques, which often introduce semantic distortions that degrade performance.

The authors acknowledge that their investigation focused on inference-time mitigation through external processing, and did not explore training-time interventions to improve intrinsic model robustness. Future work should therefore concentrate on developing noise-aware instruction tuning or adversarial training paradigms to build more resilient models capable of functioning reliably in complex real-world settings. These findings are significant as they underscore the limitations of existing models in practical applications and suggest that current evaluation methods, relying on simplified noise conditions, fail to adequately capture the challenges of real-world acoustic ecology. While the study provides a comprehensive diagnosis of vulnerabilities, the authors note that exploring training-time interventions remains an important area for future research to cultivate truly robust performance.