WavBench: Benchmarking Reasoning, Colloquialism, and Paralinguistics for End-to-End Spoken Dialogue Models

• URL: http://arxiv.org/abs/2602.12135v2
• Date: Fri, 13 Feb 2026 16:49:23 GMT
• Title: WavBench: Benchmarking Reasoning, Colloquialism, and Paralinguistics for End-to-End Spoken Dialogue Models
• Authors: Yangzhuo Li, Shengpeng Ji, Yifu Chen, Tianle Liang, Haorong Ying, Yule Wang, Junbo Li, Jun Fang, Zhou Zhao,
• Abstract summary: WavBench is a benchmark designed to evaluate realistic conversational abilities.<n>It offers insights into the intersection of complex problem-solving, colloquial delivery, and paralinguistic fidelity.
• Score: 46.528618646773175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid integration of advanced reasoning capabilities into spoken dialogue models, the field urgently demands benchmarks that transcend simple interactions to address real-world complexity. However, current evaluations predominantly adhere to text-generation standards, overlooking the unique audio-centric characteristics of paralinguistics and colloquialisms, alongside the cognitive depth required by modern agents. To bridge this gap, we introduce WavBench, a comprehensive benchmark designed to evaluate realistic conversational abilities where prior works fall short. Uniquely, WavBench establishes a tripartite framework: 1) Pro subset, designed to rigorously challenge reasoning-enhanced models with significantly increased difficulty; 2) Basic subset, defining a novel standard for spoken colloquialism that prioritizes "listenability" through natural vocabulary, linguistic fluency, and interactive rapport, rather than rigid written accuracy; and 3) Acoustic subset, covering explicit understanding, generation, and implicit dialogue to rigorously evaluate comprehensive paralinguistic capabilities within authentic real-world scenarios. Through evaluating five state-of-the-art models, WavBench offers critical insights into the intersection of complex problem-solving, colloquial delivery, and paralinguistic fidelity, guiding the evolution of robust spoken dialogue models. The benchmark dataset and evaluation toolkit are available at https://naruto-2024.github.io/wavbench.github.io/.

Related papers

• On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation [88.77441715819366]
  Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content.<n>We propose a variety of likelihood- and generative-based evaluation methods that serve in place of naive global token perplexity.
  arXiv  Detail & Related papers  (2026-01-09T22:01:56Z)
• Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction [12.216811577733125]
  We introduce Audio MultiChallenge, an open-source benchmark to evaluate E2E spoken dialogue systems under natural multi-turn interaction patterns.<n>We introduce a new axis Voice Editing that tests robustness to mid-utterance speech repairs and backtracking.<n>We curate 452 conversations from 47 speakers with 1,712 instance-specific rubrics through a hybrid audio-native agentic and human-in-the-loop pipeline.
  arXiv  Detail & Related papers  (2025-12-16T19:26:44Z)
• MultiVox: A Benchmark for Evaluating Voice Assistants for Multimodal Interactions [70.93364531054273]
  We introduce MultiVox, the first benchmark to evaluate the ability of voice assistants to integrate spoken and visual cues.<n>Specifically, MultiVox includes 1000 human-annotated and recorded speech dialogues that encompass diverse paralinguistic features.<n>Our evaluation on 10 state-of-the-art models reveals that, although humans excel at these tasks, current models consistently struggle to produce contextually grounded responses.
  arXiv  Detail & Related papers  (2025-07-14T23:20:42Z)
• Aligning Spoken Dialogue Models from User Interactions [55.192134724622235]
  We propose a novel preference alignment framework to improve spoken dialogue models on realtime conversations from user interactions.<n>We create a dataset of more than 150,000 preference pairs from raw multi-turn speech conversations annotated with AI feedback.<n>Our findings shed light on the importance of a well-calibrated balance among various dynamics, crucial for natural real-time speech dialogue systems.
  arXiv  Detail & Related papers  (2025-06-26T16:45:20Z)
• Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue [71.15186328127409]
  Paralinguistics-enhanced Generative Pretrained Transformer (ParalinGPT) Model takes the conversational context of text, speech embeddings, and paralinguistic attributes as input prompts within a serialized multitasking framework. We utilize the Switchboard-1 corpus, including its sentiment labels as the paralinguistic attribute, as our spoken dialogue dataset.
  arXiv  Detail & Related papers  (2023-12-23T18:14:56Z)
• Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling [70.23876429382969]
  We propose a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks. Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena. For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge.
  arXiv  Detail & Related papers  (2023-07-16T15:18:25Z)
• Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization [4.226093500082746]
  We introduce self-supervised methods to compensate shortcomings to train a dialogue summarization model. Our principle is to detect incoherent information flows using pretext dialogue text to enhance BERT's ability to contextualize the dialogue text representations.
  arXiv  Detail & Related papers  (2022-09-01T07:51:46Z)
• Robustness Testing of Language Understanding in Dialog Systems [33.30143655553583]
  We conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models. We introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. We propose a model-agnostic toolkit LAUG to approximate natural perturbation for testing the robustness issues in dialog systems.
  arXiv  Detail & Related papers  (2020-12-30T18:18:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.