Audio-Language Model Evaluation

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The field measures how well models use audio to answer questions, follow spoken instructions, describe sounds, and reason about events over time. Researchers build benchmarks for speech, music, environmental sound, long recordings, multilingual settings, and safety, using multiple-choice and open-ended tasks alongside human ratings. Controlled tests—such as removing or altering audio, changing answer order, or making speech conflict with text—help reveal shortcuts, bias, and whether a model’s answer is actually grounded in sound; uncertainty estimates and model-based judges can make evaluation more scalable. A central challenge is that strong benchmark scores may coexist with weak acoustic grounding, temporal reasoning, and robustness to changes in wording, speakers, or recording conditions.

Latest papers

206 papers33 papers in the last four weeks, level with the four weeks before. 0.2% of all new papers.

Mar 18, 2026

Multi-Source Evidence Fusion for Audio Question Answering

Large audio language models (LALMs) can answer questions about speech, music, and environmental sounds, yet their internal reasoning is largely opaque and difficult to validate. We describe TalTech's solution to the Agent Track of the Interspeech 2026 Audio Reasoning Challenge, in which systems are evaluated on reasoning process quality, specifically the factual accuracy, logical soundness, and completeness of their reasoning chains. Our multi-source ensemble pipeline uses two LALMs that generate independent observations, while a separate text-only reasoning model cross-checks these against outputs from 25 acoustic tools organized into reliability tiers. By grounding every inference step in explicit, reliability-tagged evidence, the system produces dense, verifiable reasoning chains. Our system ranked first in the challenge, outperforming all competing systems by a wide margin in challenge's reasoning quality metric.

Aivo Olev, Tanel Alumäe

Tallinn University of Technology, Estonia

Audio QA Audio Reasoning Audio-Language Model Evaluation

Mar 17, 2026

DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models

Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuinely process acoustic signals or rely on text-based semantic inference. To systematically study this question, we introduce DEAF (Diagnostic Evaluation of Acoustic Faithfulness), a benchmark of over 2,700 conflict stimuli spanning three acoustic dimensions: emotional prosody, background sounds, and speaker identity. Then, we design a controlled multi-level evaluation framework that progressively increases textual influence, ranging from semantic conflicts in the content to misleading prompts and their combination, allowing us to disentangle content-driven bias from prompt-induced sycophancy. We further introduce diagnostic metrics to quantify model reliance on textual cues over acoustic signals. Our evaluation of seven Audio MLLMs reveals a consistent pattern of text dominance: models are sensitive to acoustic variations, yet predictions are predominantly driven by textual inputs, revealing a gap between high performance on standard speech benchmarks and genuine acoustic understanding.

Jiaqi Xiong, Yunjia Qi, Qi Cao +6

University of Oxford · XJTLU · HNU +3

Audio-Language Model Evaluation Audio-Language Models Audio Understanding

Mar 15, 2026

PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark

Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching, none of which is captured by existing benchmarks. We introduce PARSA-Bench (Persian Audio Reasoning and Speech Assessment Benchmark), the first dedicated benchmark for evaluating LALMs on Persian language and culture. It covers 16 tasks, ten of them new, spanning speech understanding, paralinguistic analysis, and culturally grounded audio reasoning. Across most tasks, text-only baselines outperform their audio counterparts, so audio understanding rather than language knowledge remains the main limitation, and supplying the transcript alongside the audio lifts weak models to near their text-only level. The consistent exception is Persian poetry, where prosody carries information the written form cannot: audio beats text on both poetry tasks, and metre detection shows the first signs of being learnable only at the largest model scale. The dataset is publicly available at: https://huggingface.co/datasets/MohammadJRanjbar/PARSA-Bench

Mohammad Javad Ranjbar Kalahroodi, Mohammad Amini, Parmis Bathayan +2

School of Electrical and Computer Engineering, University of Tehran, Iran · Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

Audio-Language Model Evaluation Audio Reasoning Audio Understanding Audio-Language Models

Mar 10, 2026

Do What I Say: A Spoken Prompt Dataset for Instruction-Following

Speech Large Language Models (SLLMs) have rapidly expanded, supporting a wide range of tasks. These models are typically evaluated using text prompts, which may not reflect real-world scenarios where users interact with speech. To address this gap, we introduce DoWhatISay (DOWIS), a multilingual dataset of human-recorded spoken and written prompts designed to pair with any existing benchmark for realistic evaluation of SLLMs under spoken instruction conditions. Spanning 9 tasks and 11 languages, it provides 10 prompt variants per task-language pair, across five styles. Using DOWIS, we benchmark state-of-the-art SLLMs, analyzing the interplay between prompt modality, style, language, and task type. Results show that text prompts consistently outperform spoken prompts, particularly for low-resource and cross-lingual settings. Only for tasks with speech output, spoken prompts do close the gap, highlighting the need for speech-based prompting in SLLM evaluation.

Maike Züfle, Sara Papi, Fabian Retkowski +5

Karlsruhe Institute of Technology, Germany · Fondazione Bruno Kessler, Italy · ACC Cyfronet AGH, Poland +2

Audio-Language Model Evaluation Speech Language Models Multilingual Language Model Evaluation

Mar 10, 2026

SCENEBench: An Audio Understanding Benchmark Grounded in Assistive and Industrial Use Cases

Advances in large language models (LLMs) have enabled significant capabilities in audio processing, resulting in state-of-the-art models now known as Large Audio Language Models (LALMs). However, minimal work has been done to measure audio understanding beyond automatic speech recognition (ASR). This paper closes that gap by proposing a benchmark suite, SCENEBench (Spatial, Cross-lingual, Environmental, Non-speech Evaluation), that targets a broad form of audio comprehension across four real-world categories: background sound understanding, noise localization, cross-linguistic speech understanding, and vocal characterizer recognition. These four categories are selected based on understudied needs from accessibility technology and industrial noise monitoring. In addition to performance, we also measure model latency. The purpose of this benchmark suite is to assess audio beyond just what words are said - rather, how they are said and the non-speech components of the audio. Because our audio samples are synthetically constructed (e.g., by overlaying two natural audio samples), we further validate our benchmark against 20 natural audio items per task, sub-sampled from existing datasets to match our task criteria, to assess ecological validity. We assess five state-of-the-art LALMs and find critical gaps: performance varies across tasks, with some tasks performing below random chance and others achieving high accuracy. These results provide direction for targeted improvements in model capabilities.

Laya Iyer, Angelina Wang, Sanmi Koyejo

Stanford University · Cornell Tech

Audio Understanding Audio-Language Model Evaluation Spoken Language Understanding

Mar 10, 2026

MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models

While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored. We introduce MUGEN, a comprehensive benchmark evaluating this capability across speech, general audio, and music. Our experiments reveal consistent weaknesses in multi-audio settings, and performance degrades sharply as the number of concurrent audio inputs increases, identifying input scaling as a fundamental bottleneck. We further investigate training-free strategies and observe that Audio-Permutational Self-Consistency, which diversifies the order of audio candidates, helps models form more robust aggregated predictions, yielding up to 6.28% accuracy gains. Combining this permutation strategy with Chain-of-Thought further improves performance to 6.74%. These results expose blind spots in current LALMs and provide a foundation for evaluating complex auditory comprehension.

Chih-Kai Yang, Yun-Shao Tsai, Yu-Kai Guo +7

Graduate Institute of Communication Engineering, National Taiwan University, Taiwan · National Taiwan University, Taiwan · NTU Artificial Intelligence Center of Research Excellence (NTU AI-CoRE), Taiwan

Audio-Language Model Evaluation Audio Understanding Audio-Language Models

Mar 10, 2026

How Contrastive Decoding Enhances Large Audio Language Models

While Contrastive Decoding (CD) has been proposed to enhance Large Audio Language Models (LALMs), it has not been evaluated at scale, and the underlying mechanisms driving its success remain unclear. This study systematically evaluates four distinct CD strategies across diverse LALM architectures. We identify Audio-Aware Decoding and Audio Contrastive Decoding as the most effective methods. However, their impact varies significantly across models. To explain this variability, we profile the baseline error composition of each model and measure how readily contrastive decoding corrects each error type. Our analysis demonstrates that CD reliably rectifies errors in which models falsely claim an absence of audio or resort to uncertainty-driven guessing, but is relatively poor at correcting flawed reasoning or confident misassertions. Crucially, CD's benefit closely tracks the composition of a model's baseline error profile: when errors caused by audio ignorance or uncertainty-driven guessing constitute only a small fraction of a model's errors, gains are marginal or even negative. A token-level analysis reveals the underlying mechanism: when the amateur's output is dominated by hesitation markers, CD's suppression naturally targets uncertainty-driven errors while having limited effect on confident misassertions.

Tzu-Quan Lin, Wei-Ping Huang, Yi-Cheng Lin +1

Graduate Institute of Communication Engineering, National Taiwan University, Taiwan · NTU Artificial Intelligence Center of Research Excellence (NTU AI-CoRE), Taiwan

Contrastive Decoding Audio-Language Models Audio-Language Model Evaluation

Mar 6, 2026

Are Audio-Language Models Listening? Audio-Specialist Heads for Adaptive Audio Steering

Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs. One example is large audio-language models (LALMs) where decisive audio evidence can be under-utilized even when it contains important information. To address this issue we use mechanistic interpretability to identify a small set of audio-specialist attention heads whose audio attention yields a ``listening'' signal. We show that this signal increases when audio evidence affects the model's output, providing an indicator of audio engagement under standard prompting. Leveraging this localization, we construct an audio--silence steering direction and apply an inference-time activation intervention to the final representation, amplifying the model's audio effect. To demonstrate the utility of this intervention, we show on MMAU that this improves accuracy by up to +8.0 percentage points on two Qwen-based LALMs, without any parameter updates.

Neta Glazer, Lenny Aharon, Ethan Fetaya

Bar-Ilan University, Ramat Gan, Israel · Columbia University, New York, NY, USA

Audio-Language Models Audio-Language Model Evaluation Causal Interventions in Language Models

Mar 5, 2026

PolyBench: A Benchmark for Compositional Reasoning in Polyphonic Audio

Large Audio Language Models (LALMs) are increasingly capable of reasoning over audio, yet existing benchmarks offer limited coverage of reasoning in polyphonic audio, where multiple sound events co-occur and induce compositional structure. To address this gap, we introduce PolyBench, a benchmark designed to evaluate compositional reasoning in polyphonic audio, comprising five evaluation subsets that cover counting, classification, detection, concurrency, and duration estimation, all of which require reasoning over multiple concurrent events and their relations. Our evaluation of state-of-the-art LALMs reveals consistent performance degradation in polyphonic settings, indicating a fundamental bottleneck in current LALMs.

Yuanjian Chen, Yang Xiao, Han Yin +3

Harbin University of Science and Technology, China · The University of Melbourne, Australia · KAIST, South Korea +1

Audio Reasoning Audio-Language Model Evaluation Compositional Reasoning Audio-Language Models

Mar 4, 2026

A Sensitivity Analysis of Multi-Event Audio Grounding in Audio LLMs

Audio LLMs have shown a strong ability to understand audio samples, yet their reliability in complex acoustic scenes remains under-explored. Unlike prior work limited to small scale or less controlled query construction, we present a large-scale evaluation of event grounding and false alarms as auditory scene complexity increases. Using 71K AudioCapsV2 clips, we extract normalized (source, attribute) events and build two query types: present-event queries for ground-truth detection and absent-event queries to probe hallucinations, using similarity-filtered negative sampling in an audio-aligned text embedding space. We evaluate four SOTA Audio LLMs with 12 prompt variants over 500K yes/no queries per model. Across models, increasing event count consistently lowers true-positive rate and raises false-positive rate, while prompts induce a strong trade-off between the two. Our confidence analysis shows that models become more uncertain on multi-event audio, revealing room for improvement.

Taehan Lee, Jaehan Jung, Hyukjun Lee

Sogang University, South Korea

Audio-Language Model Evaluation Audio-Language Models Audio-Language Model Hallucination Detection

Mar 2, 2026

Geometric Alignment without Functional Equivalence: A Layer-wise Analysis of the Speech-Text Modality Gap

End-to-end speech large language models (LLMs) often answer the same question less accurately from speech than from text, even when the spoken content matches the text prompt. Existing analyses study this modality gap through the geometry of representations and measure how closely speech representations align with text. Geometric alignment gives the gap a measurable form, but it does not show whether the model uses aligned representations in the same way. This work connects representation geometry with answer formation and asks whether geometric alignment implies functional equivalence. We compare cross-layer Centered Kernel Alignment (CKA) with layer-wise answer margins for four open-weight speech LLMs on SpeechMMLU and VoiceBench BBH. In Qwen2.5-Omni and MiniCPM-o, speech and text representations align in middle layers while their late-layer answer margins still differ, so mid-layer alignment does not imply functional equivalence. Qwen2-Audio instead shows weak alignment, which indicates a different failure mode. An attention intervention at fixed input and token count identifies a causal effect of late attention allocation on answer-code separation, a quantity distinct from answer correctness. Compressing the speech signal or its token sequence repairs a subset of the errors that occur only with speech.

Ming-Hao Hsu, Xiaohai Tian, Jun Zhang +3

School of Data Science, The Chinese University of Hong Kong, Shenzhen · ByteDance

Speech Language Models Audio-Language Model Evaluation

Feb 28, 2026

Voices of Civilizations: A Multilingual QA Benchmark for Global Music Understanding

We introduce Voices of Civilizations, the first multilingual QA benchmark for evaluating audio LLMs' cultural comprehension on full-length music recordings. Covering 380 tracks across 38 languages, our automated pipeline yields 1,190 multiple-choice questions through four stages - each followed by manual verification: 1) compiling a representative music list; 2) generating cultural-background documents for each sample in the music list via LLMs; 3) extracting key attributes from those documents; and 4) constructing multiple-choice questions probing language, region associations, mood, and thematic content. We evaluate models under four conditions and report per-language accuracy. Our findings demonstrate that even state-of-the-art audio LLMs struggle to capture subtle cultural nuances without rich textual context and exhibit systematic biases in interpreting music from different cultural traditions. The dataset is publicly available on Hugging Face to foster culturally inclusive music understanding research.

Shangda Wu, Ziya Zhou, Yongyi Zang +4

Independent Researcher · The Hong Kong University of Science and Technology, Hong Kong SAR, China · The Chinese University of Hong Kong, Hong Kong SAR, China

Music Understanding Audio-Language Model Evaluation Cultural Knowledge in Language Models Multilingual QA Cultural Bias in Language Models Music Information Retrieval

Feb 27, 2026

AudioCapBench: Quick Evaluation on Audio Captioning across Sound, Music, and Speech

We introduce AudioCapBench, a benchmark for evaluating audio captioning capabilities of large multimodal models. \method covers three distinct audio domains, including environmental sound, music, and speech, with 1,000 curated evaluation samples drawn from established datasets. We evaluate 13 models across two providers (OpenAI, Google Gemini) using both reference-based metrics (METEOR, BLEU, ROUGE-L) and an LLM-as-Judge framework that scores predictions on three orthogonal dimensions: accuracy (semantic correctness), completeness (coverage of reference content), and hallucination (absence of fabricated content). Our results reveal that Gemini models generally outperform OpenAI models on overall captioning quality, with Gemini3Pro achieving the highest overall score (6.00/10), while OpenAI models exhibit lower hallucination rates. All models perform best on speech captioning and worst on music captioning. We release the benchmark as well as evaluation code to facilitate reproducible audio understanding research.

Jielin Qiu, Jianguo Zhang, Zixiang Chen +13

Salesforce AI Research

Audio-Language Model Evaluation Audio Captioning

Feb 25, 2026

Evaluation of Audio Language Models for Fairness, Safety, and Security

Audio large language models (ALLMs) have recently advanced spoken interaction by integrating speech processing with large language models. However, existing evaluations of fairness, safety, and security (FSS) remain fragmented, largely because ALLMs differ fundamentally in how acoustic information is represented and where semantic reasoning occurs. Differences that are rarely made explicit. As a result, evaluations often conflate structurally distinct systems, obscuring the relationship between model design and observed FSS behavior. In this work, we introduce a structural taxonomy (system-level and representational) of ALLMs that categorizes systems along two axes: the form of audio input representation (e.g., discrete vs. continuous) and the locus of semantic reasoning (e.g., cascaded, multimodal, or audio-native). Building on the taxonomy, we propose a unified evaluation framework that assesses semantic invariance under paralinguistic variation, refusal and toxicity behavior under unsafe prompts, and robustness to adversarial audio perturbations. We apply this framework to two representative systems and observe systematic differences in refusal rates, attack success, and toxicity between audio and text inputs. Our findings demonstrate that FSS behavior is tightly coupled to how acoustic information is integrated into semantic reasoning, underscoring the need for structure-aware evaluation of audio language models.

Ranya Aloufi, Srishti Gupta, Soumya Shaw +2

Audio-Language Model Evaluation Audio-Language Models LLM Security Adversarial Robustness of Language Models

Feb 19, 2026

The Cascade Equivalence Hypothesis: When Do Speech LLMs Behave Like ASR $\rightarrow$ LLM Pipelines?

Speech LLMs are widely understood to be better than ASR

\rightarrow

LLM cascades since they have access to the audio directly, and not just the transcript. In this paper, we present an evaluation methodology and a mechanistic interpretation of the observed behavior of speech LLMs. First, we introduce matched-backbone testing which separates out the behavior of the speech LLM from the reasoning capabilities of the underlying LLM. Second, we provide a mechanistic analysis of speech LLMs using logit lens and LEACE and show the literal transcript emerging from the LLM's hidden states and that text representations are causally necessary. We also show that in most deployed use cases, current speech LLMs are expensive cascades, and under noise, they are worse ones, with clean-condition advantages reversing by up to 7.6% at 0dB.

Jayadev Billa

San Jose CA, USA.

Speech Language Models Audio-Language Model Evaluation Automatic Speech Recognition

Feb 17, 2026

MAEB: Massive Audio Embedding Benchmark

We introduce the Massive Audio Embedding Benchmark (MAEB), a large-scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross-modal audio-text reasoning in 100+ languages. We evaluate 50+ models and find that no single model dominates across all tasks: contrastive audio-text models excel at environmental sound classification (e.g., ESC50) but score near random on multilingual speech tasks (e.g., SIB-FLEURS), while speech-pretrained models show the opposite pattern. Clustering remains challenging for all models, with even the best-performing model achieving only modest results. We observe that models excelling on acoustic understanding often perform poorly on linguistic tasks, and vice versa. We also show that the performance of audio encoders on MAEB correlates highly with their performance when used in audio large language models. MAEB is derived from MAEB+, a collection of 98 tasks. MAEB is designed to maintain task diversity while reducing evaluation cost, and it integrates into the MTEB ecosystem for unified evaluation across text, image, and audio modalities. We release MAEB and all 98 tasks along with code and a leaderboard at https://github.com/embeddings-benchmark/mteb.

Adnan El Assadi, Isaac Chung, Chenghao Xiao +15

Carleton University · Zendesk · Durham University +7

Audio Understanding Audio-Language Model Evaluation

Feb 4, 2026

RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity

As conversational multimodal AI tools are increasingly adopted to process patient data for health assessment, robust benchmarks are needed to measure progress and expose failure modes under realistic conditions. Despite the importance of respiratory audio for mobile health screening, respiratory audio question answering remains underexplored, with existing studies evaluated narrowly and lacking real-world heterogeneity across modalities, devices, and question types. We hence introduce the Respiratory-Audio Question-Answering (RA-QA) benchmark, including a standardized data generation pipeline, a comprehensive multimodal QA collection, and a unified evaluation protocol. RA-QA harmonizes public RA datasets into a collection of 9 million format-diverse QA pairs covering diagnostic and contextual attributes. We benchmark general audio-language models as well as domain-specific architectures, establishing reproducible reference points and showing how current approaches fail under heterogeneity.

Gaia A. Bertolino, Yuwei Zhang, Tong Xia +2

University of Cambridge · Cambridge, United Kingdom · Tsinghua University +3

Audio-Language Model Evaluation Audio QA Healthcare Audio Understanding Multimodal QA Audio-Language Models+2

Jan 29, 2026

SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding

Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, while their ability to process sequential audio-video data remains underexplored. This gap highlights the need for a high-quality benchmark to systematically evaluate MLLM performance in a real-world setting. We introduce SONIC-O1, a comprehensive, fully human-verified benchmark of 60 hours (231 clips) spanning 13 real-world conversational domains with 4,958 annotations and demographic metadata. SONIC-O1 evaluates three capabilities: open-ended summarization, multiple-choice question (MCQ) answering, and temporal localization with supporting rationales (reasoning). Across closed- and open-source models, we find that the MCQ accuracy shows the smallest gap between model families, but the best closed-source model outperforms the best open-source model by 22.6% on temporal localization. We further observe accuracy gaps of up to 21.4% on temporal localization across demographic groups, indicating persistent disparities in model behaviour. SONIC-O1 provides an open evaluation suite for temporally grounded and demographically robust multimodal understanding. SONIC-O1 is publicly available for research: Project page (https://vectorinstitute.github.io/sonic-o1/), Dataset (https://huggingface.co/datasets/vector-institute/sonic-o1), GitHub (https://github.com/vectorinstitute/sonic-o1), Leaderboard (https://huggingface.co/spaces/vector-institute/sonic-o1-leaderboard).

Ahmed Y. Radwan, Christos Emmanouilidis, Hina Tabassum +2

Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, ON M5G 1L7, Canada · University of Groningen, Nijenborgh 4, 9747 AG Groningen, Netherlands · York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada

Multimodal Understanding Multimodal Grounding Audio-Visual Understanding Video-Language Model Evaluation Audio Understanding Language Model Bias Evaluation+4

Nov 28, 2025

ORCA: Open-ended Response Correctness Assessment for Audio Question Answering

Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art. As benchmarks rapidly evolve to incorporate complex reasoning and subjective tasks, they increasingly necessitate open-ended responses from LALMs. We present Open-ended Response Correctness Assessment (ORCA) -- a reliable and lightweight model-based approach for answer correctness and disagreement modeling. We employ a three-stage annotation pipeline combining human judgment, structured feedback, and human-AI correction, yielding 9,663 annotations across 3,699 question-answer pairs from 15 LALMs on three audio understanding and reasoning benchmarks (achieving a Krippendorff's alpha of 0.82). Our experiments employing curriculum learning show that ORCA models achieve a Spearman correlation of 0.91 with average human correctness ratings on seen benchmarks and generalize to unseen benchmarks with a score of 0.85, outperforming several LLM judge baselines including Gemini 2.5 Flash. Furthermore, we demonstrate that ORCA's predicted variance correlates strongly with human disagreement, allowing it to effectively identify problematic benchmark items.

Šimon Sedláček, Sara Barahona, Bolaji Yusuf +9

AUDIAS, Universidad Autónoma de Madrid, Spain · University of Buenos Aires, Argentina · Tufts University, USA +1

Audio-Language Model Evaluation LLM Evaluation Audio QA LLM-as-a-Judge Audio Understanding Audio-Language Models+1

Nov 20, 2025

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation

Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored. Crucially, there is no consensus on whether audio-language models can build effective general-purpose audio encoders, nor a systematic understanding of how pretraining objectives behave across diverse tasks and scales. We identify three key barriers: limited scale of audio-text corpora, limited coverage of audio attributes in existing caption corpora, and lack of systematic exploration and evaluation. To fill this gap, we present the first principled empirical study of ALP. We first introduce CaptionStew, a 10.7M caption dataset aggregating open-source audio-text corpora across multiple domains and captioning focuses. We then conduct the first comprehensive evaluation comparing contrastive and captioning objectives for learning audio representation across speech, music, and environmental sound tasks. Our results not only demonstrate that ALP yields competitive, transferable representations, but reveal critical trade-offs: contrastive learning offers superior data efficiency, while captioning exhibits better scalability. Furthermore, we find that the benefits of supervised initialization often diminish at larger scales, challenging common practices. By grounding these claims in empirical evidence, we establish a viable pathway toward general-purpose audio representation learning, guiding future research.

Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo +3

University of Texas at Austin · Tencent AI Lab Seattle · Zhejiang University +1

Audio-Language Model Evaluation Audio Representation Learning Audio Understanding Audio-Language Models Audio Captioning Multimodal Contrastive Learning

Oct 13, 2025

VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents

Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks remain limited -- they are mainly English-centric, rely on synthetic speech, and lack comprehensive, discriminative evaluation across multiple dimensions. To address these gaps, we present Voice Chat Bot Bench (VCB Bench) -- a high-quality Chinese benchmark built entirely on real human speech. VCB Bench evaluates LALMs from three complementary perspectives: instruction following (including speech-level control beyond text commands), knowledge understanding (general knowledge, reasoning, and daily dialogue), and robustness (stability under perturbations in content, environment, and speaker traits). Experiments on representative LALMs reveal notable performance gaps and highlight future directions for improvement. VCB Bench provides a reproducible and fine-grained evaluation framework, offering standardized methodology and practical insights for advancing Chinese voice conversational models.

Jiliang Hu, Wenfu Wang, Zuchao Li +6

School of Artificial Intelligence, Wuhan University, Wuhan, China · Tencent AI Lab, Beijing, China

Audio-Language Model Evaluation Voice Agent Evaluation Audio-Language Models Speech Language Models Language Model Robustness

Oct 6, 2025

Robustness assessment of large audio language models in multiple-choice evaluation

Recent advances in large audio language models (LALMs) have primarily been assessed using a multiple-choice question answering (MCQA) framework. However, subtle changes, such as shifting the order of choices, result in substantially different results. Existing MCQA frameworks do not account for this variability and report a single accuracy number per benchmark or category. We dive into the MCQA evaluation framework and conduct a systematic study spanning three benchmarks (MMAU, MMAR and MMSU) and four models: Audio Flamingo 2, Audio Flamingo 3, Qwen2.5-Omni-7B-Instruct, and Kimi-Audio-7B-Instruct. Our findings indicate that models are sensitive not only to the ordering of choices, but also to the paraphrasing of the question and the choices. Finally, we propose a simpler evaluation protocol and metric that account for subtle variations and provide a more detailed evaluation report of LALMs within the MCQA framework.

Fernando López, Santosh Kesiraju, Jordi Luque

Scientific Research, Telef´onica Innovaci´on Digital, Spain · Brno University of Technology, Czech Republic

Audio-Language Model Evaluation Audio QA Multiple-Choice Question Answering Audio-Language Models Language Model Robustness

Oct 1, 2025

Hearing the Order: Investigating Position Bias in Large Audio-Language Models

Large audio-language models (LALMs) are often used in tasks that involve reasoning over ordered options. An open question is whether their predictions are influenced by the order of answer choices, which would indicate a form of position bias and undermine their reliability. In this paper, we identify and analyze this problem in LALMs. We demonstrate that no model is immune to this bias through extensive experiments on six LALMs across three widely used benchmarks and their spoken counterparts. Shuffling the order of answer options can cause performance fluctuations of up to 24% and even change model rankings, raising concerns about the reliability of current evaluation practices. We also study permutation-based strategies and show that they can mitigate bias in most cases. Our work represents the first systematic investigation of this issue in LALMs, and we hope it raises awareness and motivates further research in this direction.

Yu-Xiang Lin, Chen-An Li, Sheng-Lun Wei +3

National Taiwan University

Audio-Language Model Evaluation Audio-Language Models Position Bias Input Order Sensitivity in Language Models

Sep 18, 2025

Evaluating Multimodal Large Language Models on Spoken Sarcasm Detection

Sarcasm detection remains challenging because sarcastic intent often depends on information across text, speech, and vision. We systematically evaluate large language models (LLMs) and multimodal LLMs (MLLMs) for sarcasm detection on English (MUStARD++) and Chinese (MCSD), considering both feature-extraction settings and direct classification. In the feature-extraction settings, audio provides the strongest unimodal performance on both datasets, while multimodal fusion shows dataset-dependent benefits. In direct classification, Qwen2.5-Omni achieves the strongest zero-shot performance among the evaluated models, with further substantial gains after fine-tuning. Error analysis further shows that additional modalities can both correct and introduce errors, highlighting the need for selective multimodal integration.

Zhu Li, Xiyuan Gao, Yuqing Zhang +2

University of Groningen, The Netherlands

MLLM Evaluation Sarcasm Detection Multimodal Model Evaluation Modality Reliance in Multimodal Models Audio-Language Model Evaluation

Sep 9, 2025

AU-Harness: An Open-Source Toolkit for Efficient and Unified Evaluation of AUdio-LLMs

Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient and nonstandardized toolkits that limit fair comparison and systematic assessment. Existing evaluation frameworks exhibit three critical limitations: (1) slow and inefficient processing pipeline that bottlenecks large-scale studies, (2) inadequate multi-turn dialogue support, leaving fundamental questions about cross-turn context integration and performance dynamics over extended conversations in LALMs unanswered; and (3) the absence of unified and scalable evaluation framework capable of keeping pace with the rapid growth of both LALMs and audio benchmarks. To address these issues, we introduce AU-Harness, an efficient and comprehensive evaluation framework for LALMs. Our empirical studies demonstrate that AU-Harness achieves 77% reduction in evaluation processing time over existing evaluation toolkits, enabling large-scale LALM evaluations previously considered impractical. We provide standardized evaluation protocols and flexible configurations for fair model comparison across diverse scenarios. AU-Harness unlocks a range of in-depth analyses difficult to conduct without a unified foundation, including multi-turn dialogue dynamics, enabling the study of true audio reasoning capabilities in existing LALMs. AU-Harness provides both practical evaluation tools and insights into model limitations, advancing systematic LALM development.

Hoang Nguyen, Sidharth Surapaneni, Akshay Kalkunte +9

ServiceNow · University of Texas at Austin

Audio-Language Model Evaluation Audio-Language Models Multi-Turn Dialogue Evaluation

Aug 28, 2025

WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations

Large audio-language models (LALMs) extend language understanding into the auditory domain, yet their ability to perform low-level listening, such as pitch and duration detection, remains underexplored. However, low-level listening is critical for real-world, out-of-distribution tasks where models must reason about unfamiliar sounds based on fine-grained acoustic cues. To address this gap, we introduce the World-of-Whale benchmark (WoW-Bench) to evaluate low-level auditory perception and cognition using marine mammal vocalizations. We use marine mammal vocalizations as out-of-distribution sound events to better assess models' low-level listening and so that the models do not rely on prior knowledge of the sound events. WoW-bench is composed of a Perception benchmark for categorizing novel sounds and a Cognition benchmark, inspired by Bloom's taxonomy, to assess the abilities to remember, understand, apply, and analyze sound events. For the Cognition benchmark, we additionally introduce distractor questions to evaluate whether models are truly solving problems through listening rather than relying on other heuristics. Experiments with state-of-the-art LALMs show performance far below human levels, indicating a need for stronger auditory grounding in LALMs.

Jaeyeon Kim, Heeseung Yun, Sang Hoon Woo +2

Carnegie Mellon University · Seoul National University · NVIDIA

Audio-Language Model Evaluation Audio Understanding