mlsanityData to 7 Oct 2026

StyleBench: Evaluating Speech Language Models on Conversational Speaking Style Control

arXiv 2603.07599 ↗First posted 8 Mar 2026cs.CLcited by 1

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Abstract (arXiv)

Speech language models (SLMs) have significantly extended the interactive capability of text-based Large Language Models (LLMs) by incorporating paralinguistic information. For more realistic interactive experience with customized styles, current SLMs have managed to interpret and control speaking style intensity from user prompts during the dialogue process. However, there remains a lack of systematic benchmarks that quantifies and evaluates the style intensity control ability in conversations. In this paper, we propose StyleBench, a multi-turn dialogue benchmark for comprehensively evaluating the style intensity control ability across four dimensions: emotion, speed, volume, and pitch. Our results reveal the performance gaps between leading SLMs and omni language models (OLMs), suggesting the underlying reasons and promising approaches for future exploration.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 31 references that are in the map. First posted on arXiv 8 Mar 2026.

Builds on

29 of 31 references.

References · 29

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2024GPT-4o System Cardcited by 4,467
  3. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 710
  4. 2025Qwen2.5-Omni Technical Reportcited by 704
  5. 2024MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategiescited by 694
  6. 2024Moshi: a speech-text foundation model for real-time dialoguecited by 536
  7. 2024CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokenscited by 323
  8. 2024CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Modelscited by 282
  9. 2024GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbotcited by 218
  10. 2024AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehensioncited by 215
  11. 2024Mini-Omni: Language Models Can Hear, Talk While Thinking in Streamingcited by 204
  12. 2023emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representationcited by 196
17 more
  1. 2024Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLMcited by 135
  2. 2024AudioBench: A Universal Benchmark for Audio Large Language Modelscited by 124
  3. 2025On The Landscape of Spoken Language Models: A Comprehensive Surveycited by 116
  4. 2025Baichuan-Omni-1.5 Technical Reportcited by 107
  5. 2024Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilitiescited by 104
  6. 2024Recent Advances in Speech Language Models: A Surveycited by 85
  7. 2025MinMo: A Multimodal Large Language Model for Seamless Voice Interactioncited by 79
  8. 2025LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesiscited by 75
  9. 2024A Survey on Speech Large Language Models for Understandingcited by 71
  10. 2024SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Trainingcited by 58
  11. 2024Controllable Text Generation for Large Language Models: A Surveycited by 55
  12. 2024EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotionscited by 44
  13. 2022Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Datasetcited by 36
  14. 2024Benchmarking Open-ended Audio Dialogue Understanding for Large Audio-Language Modelscited by 28
  15. 2021ASR4REAL: An extended benchmark for speech modelscited by 14
  16. 2025DeepDialogue: A Multi-Turn Emotionally-Rich Spoken Dialogue Datasetcited by 8
  17. 2025SageLM: A Multi-aspect and Explainable Large Language Model for Speech Judgementcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On the Emotion Understanding of Synthesized Speechcited by 0