NonverbalTTS Dataset 🎵🗣️

NonverbalTTS is a 17-hour open-access English speech corpus with aligned text annotations for nonverbal vocalizations (NVs) and emotional categories, designed to advance expressive text-to-speech (TTS) research.

Key Features ✨

• 17 hours of high-quality speech data
• 10 NV types: Breathing, laughter, sighing, sneezing, coughing, throat clearing, groaning, grunting, snoring, sniffing
• 8 emotion categories: Angry, disgusted, fearful, happy, neutral, sad, surprised, other
• Diverse speakers: 2296 speakers (60% male, 40% female)
• Multi-source: Derived from VoxCeleb and Expresso corpora
• Rich metadata: Emotion labels, NV annotations, speaker IDs, audio quality metrics
• Sampling rate: 16kHz for audio from VoxCeleb, 48kHz for audio from Expresso

  Loading the Dataset 💻

  from datasets import load_dataset
  
  dataset = load_dataset("deepvk/NonverbalTTS")
  

  Annotation Pipeline 🔧

  1. Automatic Detection

     • NV detection using BEATs
     • Emotion classification with emotion2vec+
     • ASR transcription via Canary model

  2. Human Validation

     • 3 annotators per sample
     • Filtered non-English/multi-speaker clips
     • NV/emotion validation and refinement

  3. Fusion Algorithm

     • Majority voting for final transcriptions
     • Pyalign-based sequence alignment
     • Multi-annotator hypothesis merging

  Benchmark Results 📊

  Fine-tuning CosyVoice-300M on NonverbalTTS achieves parity with state-of-the-art proprietary systems:

  Metric	NVTTS	CosyVoice2
  Speaker Similarity	0.89	0.85
  NV Jaccard	0.8	0.78
  Human Preference	33.4%	35.4%

  Use Cases 💡

  • Training expressive TTS models
  • Zero-shot NV synthesis
  • Emotion-aware speech generation
  • Prosody modeling research

  License 📜

  • Annotations: CC BY-NC-SA 4.0
  • Audio: Adheres to original source licenses (VoxCeleb, Expresso)

  Citation 📝

  @misc{borisov2025nonverbalttspublicenglishcorpus,
        title={NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech}, 
        author={Maksim Borisov and Egor Spirin and Daria Diatlova},
        year={2025},
        eprint={2507.13155},
        archivePrefix={arXiv},
        primaryClass={cs.LG},
        url={https://arxiv.org/abs/2507.13155}, 
  }