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

Metadata Schema (metadata.csv) 📋

Column	Description	Example
index	Unique sample ID	ex01_sad_00265
file_name	Audio file path	wavs/ex01_sad_00265.wav
Emotion	Emotion label	sad
Initial text	Raw transcription	"So, Mom, 🌬️ how've you been?"
Annotator response {1,2,3}	Refined transcriptions	"So, Mom, how've you been?"
Result	Final fused transcription	"So, Mom, 🌬️ how've you been?"
dnsmos	Audio quality score (1-5)	3.936982
duration	Audio length (seconds)	3.6338125
speaker_id	Speaker identifier	ex01
data_name	Source corpus	Expresso
gender	Speaker gender	m

NV Symbols: 🌬️=Breath, 😂=Laughter, etc. (See Annotation Guidelines)

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 📝

@dataset{nonverbaltts2024,
  author = {Borisov Maksim, Spirin Egor, Dyatlova Darya},
  title = {NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech},
  month = April,
  year = 2025,
  publisher = {Zenodo},
  version = {1.0},
  doi = {10.5281/zenodo.15274617},
  url = {https://zenodo.org/records/15274617}
}