OpenNovelty.org

MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
SpeechLLMsMultimodalSpeech ProcessingLinguisticsLLM
Abstract:

Speech inherently contains rich acoustic information that extends far beyond the textual language. In real-world spoken communication, effective interpretation often requires integrating semantic meaning (e.g., content), paralinguistic features (e.g., emotions, speed, pitch) and phonological characteristics (e.g., prosody, intonation, rhythm), which are embedded in speech. While recent multimodal Speech Large Language Models (SpeechLLMs) have demonstrated remarkable capabilities in processing audio, their ability to perform fine-grained perception and complex reasoning in natural speech remains largely unexplored. To address this gap, we introduce MMSU, a comprehensive benchmark designed specifically for understanding and reasoning in speech. MMSU comprises 5,000 meticulously curated audio-question-answer triplets across 47 distinct tasks. Notably, linguistic theory forms the foundation of speech language understanding (SLU), yet existing benchmarks have paid insufficient attention to this fundamental aspect and fail to capture the broader linguistic picture. To ground our benchmark in linguistic principles, we systematically incorporate a wide range of linguistic phenomena, including phonetics, prosody, rhetoric, syntactics, semantics, and paralinguistics. Through a rigorous evaluation of 22 advanced SpeechLLMs, we identify substantial room for improvement in existing models. MMSU establishes a new standard for comprehensive assessment of SLLU, providing valuable insights for developing more sophisticated human-AI speech interaction systems.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces MMSU, a benchmark comprising 5,000 audio-question-answer triplets across 47 tasks for evaluating spoken language understanding and reasoning. Within the taxonomy, it resides in the 'Comprehensive Multi-task Spoken Language Understanding Benchmarks' leaf under 'Linguistic Reasoning and Mathematical Problem Solving'. Notably, this leaf contains no sibling papers—the original paper stands alone in this specific category. This isolation suggests the direction is relatively sparse, though the parent branch includes related work on spoken mathematical reasoning and spatial language understanding, indicating emerging interest in speech-based reasoning evaluation.

The taxonomy reveals that MMSU occupies a unique position bridging perceptual and reasoning-focused research. Neighboring leaves include 'Spoken Mathematical Reasoning Benchmarks' (focused narrowly on math tasks) and 'Spatial Language Understanding and Reasoning' (addressing spatial representations). The broader 'Computational Models and Speech Language Systems' branch contains work on paralinguistic-aware models and task-oriented SLU systems, which address acoustic features but typically not comprehensive multi-task reasoning. MMSU's emphasis on integrating linguistic theory across diverse phenomena distinguishes it from these more specialized or application-driven efforts, positioning it at the intersection of theoretical grounding and practical evaluation.

Among 30 candidates examined across three contributions, none were identified as clearly refuting the work. For the MMSU benchmark itself, 10 candidates were reviewed with 0 refutable overlaps. Similarly, the systematic integration of linguistic theory (10 candidates, 0 refutable) and the use of high-quality authentic audio with fine-grained features (10 candidates, 0 refutable) showed no substantial prior work within this limited search scope. This suggests that within the examined literature, the combination of comprehensive multi-task coverage, linguistic theory grounding, and fine-grained acoustic annotation appears relatively novel, though the search scale limits definitive conclusions.

Based on the top-30 semantic matches and taxonomy structure, the work appears to address an underexplored niche: comprehensive benchmarking that spans multiple linguistic dimensions in spoken form. The absence of sibling papers and lack of refuting candidates within the examined scope suggest potential novelty, though this assessment is constrained by the limited search breadth. A more exhaustive review would be needed to confirm whether similar multi-task, theory-grounded spoken benchmarks exist beyond the candidate pool analyzed here.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Spoken language understanding and reasoning across multiple linguistic dimensions. The field encompasses a broad spectrum of research directions organized into seven major branches. Neural and Cognitive Mechanisms of Speech Processing investigates the brain's underlying architecture for language, including work on EEG speech tracking[9] and hierarchical linguistic predictions[1]. Perceptual and Multimodal Speech Understanding addresses how listeners process speech in challenging conditions, such as noisy environments[4][7] and multi-talker scenarios[18], while also integrating visual cues[15][43]. Computational Models and Speech Language Systems focuses on building practical architectures for spoken language tasks, exemplified by systems like Goat SLM[5] and conversational speech architectures[25]. Linguistic Reasoning and Mathematical Problem Solving explores higher-level cognitive processes, including spoken mathematical reasoning[6] and everyday reasoning[49]. The remaining branches cover linguistic variation across discourse contexts[14][29][41], cross-linguistic phenomena and second language acquisition[22][30], and theoretical frameworks that unify these perspectives[8][10][32]. Particularly active lines of work reveal contrasting emphases between perceptual robustness and high-level reasoning capabilities. While many studies address acoustic challenges and multimodal integration to improve comprehension under adverse conditions, a smaller cluster targets complex reasoning tasks that require integrating linguistic input with mathematical or spatial knowledge[3][6]. The MMSU Benchmark[0] sits squarely within the Linguistic Reasoning and Mathematical Problem Solving branch, specifically targeting comprehensive multi-task evaluation of spoken language understanding. Unlike Goat SLM[5], which emphasizes general-purpose spoken language modeling, or Spoken Mathematical Reasoning[6], which focuses narrowly on math problem solving, MMSU Benchmark[0] adopts a broader evaluative stance across multiple reasoning dimensions. This positions it as a unifying assessment tool that bridges perceptual understanding with higher-order cognitive tasks, addressing open questions about how well current systems handle the full spectrum of linguistic and reasoning challenges in spoken form.

Claimed Contributions

MMSU benchmark for spoken language understanding and reasoning

The authors introduce MMSU, a new benchmark containing 5,000 audio-question-answer triplets spanning 47 tasks. It is designed to evaluate Speech Large Language Models on both perception and reasoning abilities in spoken language understanding.

10 retrieved papers
Systematic integration of linguistic theory into benchmark design

The authors claim to be the first benchmark that systematically incorporates established linguistic theories from multiple subfields (phonetics, prosody, rhetoric, syntactics, semantics, paralinguistics) into task design, grounding evaluation in theoretical principles rather than ad-hoc task selection.

10 retrieved papers
High-quality authentic audio with fine-grained acoustic features

The authors emphasize that MMSU prioritizes real-world recordings and professional studio audio over synthetic speech, capturing fine-grained acoustic features such as diverse accents, emotions, prosodic variations, non-verbal sounds, and intonation patterns to ensure authenticity.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

MMSU benchmark for spoken language understanding and reasoning

The authors introduce MMSU, a new benchmark containing 5,000 audio-question-answer triplets spanning 47 tasks. It is designed to evaluate Speech Large Language Models on both perception and reasoning abilities in spoken language understanding.

Contribution

Systematic integration of linguistic theory into benchmark design

The authors claim to be the first benchmark that systematically incorporates established linguistic theories from multiple subfields (phonetics, prosody, rhetoric, syntactics, semantics, paralinguistics) into task design, grounding evaluation in theoretical principles rather than ad-hoc task selection.

Contribution

High-quality authentic audio with fine-grained acoustic features

The authors emphasize that MMSU prioritizes real-world recordings and professional studio audio over synthetic speech, capturing fine-grained acoustic features such as diverse accents, emotions, prosodic variations, non-verbal sounds, and intonation patterns to ensure authenticity.

Table of Contents