With the rapid evolution of large-scale models, AI's interactive capabilities have reached unprecedented heights. However, achieving truly natural and empathetic human-computer interaction requires more than just understanding words—it requires recognizing and responding to human emotions. The shortage of high-quality, emotionally rich, multi-speaker speech data remains a critical bottleneck in advancing these capabilities.
To address this challenge, Magic Data has officially released the “Multi-Speaker Emotional Speech Dataset” on MagicHub.com. This dataset provides high-fidelity and expressively annotated speech resources essential for training emotional modeling and enhancing large language models (LLMs).
Empowering Next-Generation AI with Emotional Intelligence
As affective computing becomes central to enhancing model performance and user experience, this dataset offers state-of-the-art support for researchers and developers working at the intersection of emotion and AI.
Enabling Emotional Perception and Expression in Large Models
While large language models perform impressively in textual understanding, they often lack emotional nuance in spoken interactions. Fine-tuning with this dataset significantly improves their capacity to detect and reproduce emotional prosody, enabling AI to move beyond mechanical responses toward emotionally intelligent dialogue.
Realistic Emotional Speech Synthesis
Traditional TTS systems often produce monotonous output lacking emotional dynamics. This dataset, featuring diverse speakers and emotional labels, is an ideal foundation for training next-generation emotional speech synthesis models. Developers can generate speech with nuanced emotional tones—such as joy, anger, sorrow, and surprise—based on text and emotional tags, with broad applicability in audiobooks, digital assistants, and virtual personas.
Boosting the Accuracy of Speech Emotion Recognition
Accurate emotional classification relies on well-labeled, high-quality datasets. With six fundamental emotions, balanced distribution, and consistent annotation, this dataset supports robust training and evaluation of speech emotion recognition models, applicable in domains such as intelligent customer service, mental health monitoring, and sentiment analysis.
概览
- Speech Samples: 1,200 Mandarin utterances
- Speakers: 10 (5 male, 5 female) with diverse vocal characteristics
- Emotion Categories: Sadness, Happiness, Surprise, Fear, Anger, Disgust
- Emotion-Text Alignment: Each utterance is semantically aligned with its emotional label to ensure consistency
Balanced Emotional Coverage
Each of the six emotions is represented by 200 utterances, providing a stable foundation for training emotionally responsive systems.
Technical Specifications
| Specification | Details |
| 语种 | Mandarin Chinese |
| Audio Format | 16kHz, 16bit, WAV |
| Channel | Mono |
| Speakers | 10 (5 male, 5 female) |
| Emotion Types | Sadness, Happiness, Surprise, Fear, Anger, Disgust |
| Total Utterances | 1,200 |
| Data Distribution | 20 utterances per speaker per emotion |
Recommended Use and Applications
Intended Users
- Researchers in speech processing and synthesis
- Multimodal AI development teams
- Affective computing and human-computer interaction (HCI) project groups
Application Domains
- Model Fine-Tuning: For training or refining emotional capabilities in pretrained models
- Cross-Speaker Generalization: Evaluating performance on previously unseen speakers
- Emotion Synthesis Evaluation: Benchmarking TTS systems for emotional fidelity
- Enhancing Conversational AI: Improving emotional responsiveness in dialogue agents
Suggested Use Cases
- Develop high-accuracy speech emotion recognition systems
- Build emotional TTS engines supporting multiple speakers
- Create emotionally aware conversational agents
- Conduct academic research, participate in algorithm competitions, and perform benchmarking
Usage Policy
- This dataset is provided solely for non-commercial academic research and technical development.
- Commercial use is strictly prohibited without explicit authorization from Magic Data.
- To obtain commercial licensing, contact the Magic Data team.
- To ensure robustness of your models, it is recommended to evaluate them across diverse environments and consider integrating this dataset with others.
Download the Multi-Speaker Emotional Speech Dataset
https://magichub.com/datasets/multi-speaker-emotional-speech-dataset/
For Large-Scale Commercial Datasets
Contact: business@magicdatatech.com
