SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation

## 📄 2604.20842v1 **作者**: Ruohan Liu, Shukang Yin, Tao Wang, Dong Zhang, Weiji Zhuang **分类**: cs.CL, cs.AI, cs.SD **发表**: 2026-04-22 ### 摘要 Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and the inherent subjectivity of assessment. To address these challenges, we introduce SpeechParaling-Bench, a comprehensive benchmark for paralinguistic-aware speech generation. It expands existing coverage from fewer than 50 to over 100 fine-grained features, supported by more than 1,000 English-Chinese parallel speech queries, and is organized into three progressively challenging tasks: fine-grained control, intra-utterance variation, and context-aware adaptation. To enable reliable evaluation, we further develop a pairwise comparison pipeline, in which candidate responses are evaluated against a fixed baseline by an LALM-based judge. By framing evaluation as relative preference rather than absolute scoring, this approach mitigates subjectivity and yields more stable and scalable assessments without costly human annotation. Extensive experiments reveal substantial limitations in current LALMs. Even leading proprietary models struggle with comprehensive static control and dynamic modulation of paralinguistic features, while failure to correctly interpret paralinguistic cues accounts for 43.3% of errors in situational dialogue. These findings underscore the need for more robust paralinguistic modeling toward human-aligned voice assistants. 🔗 arXiv 论文页面 --- 语音不只是"说了什么"，更要看"怎么说"——语速、语调、停顿这些副语言线索其实才是让AI听起来像人的关键。SpeechParaling-Bench把副语言特征从不到50个扩展到100+，还专门设计了中英双语的测试场景，以后AI声音助手有没有灵魂可能就靠这个了。

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