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