Challenge: Large Audio-Language Models (LALMs) have recently unlocked audio dialogue capabilities, enabling direct spoken exchanges with humans.
Approach: They propose to evaluate LALMs' open-ended audio dialogue ability in 3 general scenarios, 12 skills, 9 multilingual languages, and 4 categories of ambiguity handling.
Outcome: The proposed benchmark assesses the open-ended audio dialogue ability for LALMs in 3 general scenarios, 12 skills, 9 multilingual languages, and 4 categories of ambiguity handling.

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AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension (2024.acl-long)

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Challenge: Existing benchmarks for audio-centric interaction have impeded advancements in this field . AIR-Bench evaluates LALMs' ability to understand audio signals and interact with humans .
Approach: They propose a benchmark to evaluate the ability of large audio-language models to understand audio signals . they use 19 tasks with approximately 19k single-choice questions to examine single-task ability .
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Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey (2025.emnlp-main)

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Challenge: Recent advances in large audio-language models (LALMs) have expanded their impact beyond natural language processing (NLP) to multimodal domains.
Approach: They propose a systematic taxonomy for LALM evaluations, categorizing them into four dimensions based on their objectives: (1) General Auditory Awareness and Processing, (2) Knowledge and Reasoning, (3) Dialogue-oriented Ability, and (4) Fairness, Safety, and Trustworthiness.
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SCENEBench: An Audio Understanding Benchmark Grounded in Assistive and Industrial Use Cases (2026.eacl-long)

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Challenge: Existing models that measure audio comprehension beyond automatic speech recognition lack performance and latency.
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URO-Bench: Towards Comprehensive Evaluation for End-to-End Spoken Dialogue Models (2025.findings-emnlp)

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Challenge: a lack of comprehensive evaluations for SDMs in speech-to-speech (S2S) scenarios is a major challenge for end-to end spoken dialogue models.
Approach: They propose to provide an extensive evaluation framework for end-to-end spoken dialogue models (SDMs) that includes both cognitive dimensions and paralinguistic cues .
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VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents (2026.findings-acl)

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Challenge: despite advances in multimodal conversational systems, current benchmarks lack comprehensive evaluation across key dimensions.
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SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues? (2026.acl-long)

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Challenge: Large Audio-Language Models (LALMs) are a popular approach for evaluating speech quality, yet their ability to assess speaker consistency across multi-turn dialogues remains unexplored.
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Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models (2025.findings-acl)

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Challenge: Existing research focused on model-specific adversarial methods, but real-world applications demand a more generalizable approach to audio adversarials.
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C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversations (2025.emnlp-main)

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Challenge: Recent developments in spoken dialogue models have created a gap in understanding their effectiveness in comprehending and emulating human conversations.
Approach: They present a benchmark dataset which comprises 1,079 instances in English and Chinese to examine their effectiveness in emulating human conversations.
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Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs (2024.findings-emnlp)

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Challenge: Current evaluation practices of open domain dialogue systems are still highly dependent on human evaluation.
Approach: They propose to use an annotated dataset to evaluate chatbots using large language models.
Outcome: The proposed model improves over few-shot inferences on a GPT-3.5 generated dialogue dataset.
AudioBench: A Universal Benchmark for Audio Large Language Models (2025.naacl-long)

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Challenge: Existing evaluation regimes for audio large language models do not cover the breadth of their possible use cases.
Approach: They propose to use AudioBench to evaluate audio large language models . they found that no single model excels consistently across all tasks .
Outcome: The proposed evaluation targets speech understanding, audio scene understanding, and voice understanding (paralinguistic) . no single model excels consistently across all tasks, the paper found .

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