ICTNLP/MutiEmo-Test

MultiEmo-Test

MultiEmo-Test is an English evaluation set for instruction-following multi-emotion text-to-speech synthesis. It accompanies HybridEmo, a system for modeling sequential emotion trajectories and simultaneous emotion blending within an utterance.

The dataset is intended for evaluation only. It contains synthesis text, natural-language emotion instructions, emotion annotations, and prompt audio for speaker-timbre conditioning. It does not contain target synthesized speech.

Dataset Composition

MultiEmo-Test contains 720 examples in a single test split.

Task	Subset	Number of examples	Description
Emotion trajectory	1E	200	A single emotion is expressed throughout the utterance.
Emotion trajectory	2E	200	Two emotions are expressed sequentially.
Emotion trajectory	3E	200	Three emotions are expressed sequentially.
Emotion blending	2E blending	120	Two emotions are expressed simultaneously.
Total		720

The trajectory subset uses seven emotion labels:

angry, disgusted, fearful, happy, neutral, sad, and surprised.

The blending subset uses six emotion labels:

angry, disgusted, fearful, happy, sad, and surprised.

Data Format

The dataset is distributed as a JSON Lines manifest and a directory of prompt audio files:

MultiEmo-Test/
├── README.md
├── test.jsonl
└── prompt-audio/
    └── *.wav

Each line in test.jsonl contains the following fields:

Field	Type	Description
id	integer	Unique example identifier.
trajectory_type	integer or null	Number of stages for a trajectory example (1, 2, or 3); null for blending examples.
emotion_trajectory	list of strings or null	Ordered emotion labels for a trajectory example.
blended_type	string or null	Set to blended for blending examples; null for trajectory examples.
blended_emotion	list of strings or null	Two emotion labels to be expressed simultaneously.
text	string	Text to synthesize.
instruction	string	Natural-language instruction describing the intended emotional expression.
prompt_audio	string	Relative path to the speaker-timbre reference audio.
prompt_text	string	Transcript of the prompt audio.
prompt_key	string	Source key associated with the prompt audio.

Example trajectory record:

{
  "id": 1,
  "trajectory_type": 1,
  "emotion_trajectory": ["angry"],
  "blended_type": null,
  "blended_emotion": null,
  "text": "Can you believe the audacity of that person?",
  "instruction": "Read this passage with a consistently angry tone.",
  "prompt_audio": "prompt-audio/common_voice_en_509177.wav",
  "prompt_text": "The autonomous ship floated closer to receiving its flying cargo.",
  "prompt_key": "yuekai/seed_tts_cosy2::path::common_voice_en_509177.wav"
}

Loading the Dataset

After downloading the repository, the manifest can be loaded with the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={"test": "test.jsonl"},
)["test"]

example = dataset[0]
print(example["instruction"])
print(example["prompt_audio"])

The value of prompt_audio is relative to the dataset root. For example, prompt-audio/example.wav should be resolved from the directory containing test.jsonl.

Intended Use

MultiEmo-Test is designed to evaluate whether instruction-following TTS systems can:

• maintain a specified emotion throughout an utterance;
• follow two- or three-stage emotion trajectories in the requested order;
• express two target emotions simultaneously;
• preserve the speaker timbre provided by the prompt audio.

The dataset is not intended as a training corpus or as a comprehensive representation of all emotions, emotion transitions, languages, speakers, or real-world speaking conditions.

License

MultiEmo-Test is released under the Apache License 2.0.

Citation

If you use this dataset, please cite the HybridEmo paper:

@misc{zhou2026sequentialtrajectoriessimultaneousblending,
  title={Sequential Trajectories and Simultaneous Blending: Multi-Emotion Modeling for Instruction-Following TTS},
  author={Yan Zhou and Yun Hong and Yang Feng},
  year={2026},
  eprint={2608.30325},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2608.30325},
}

Project repository: https://github.com/ictnlp/HybridEmo