Rich Voice Dataset for Emotion Recognition & Speech AI

**Overview**
Emotion understanding is a crucial pillar for human-centered AI. This dataset provides 50,000 synthesized, emotion-annotated voice recordings from 500 real speaker voices, offering a high-quality audio benchmark for detecting and modeling emotional speech. It supports diverse applications such as voice assistants, mental health monitoring, empathetic AI, and voice with emotional nuance.

**Use cases**

- Emotion Recognition Training: Build or evaluate speech models that can classify emotional tone with real-world variability.
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- Expressive Text-to-Speech (TTS): Train voice synthesis models to generate speech with expressive emotional cues.
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- Mental Health & Wellness Applications: Detect signs of distress or emotional shifts through vocal cues in real-time.
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- Accessibility & Social Robotics: Enable more emotionally aware human-computer interactions.

**Product details**

- Metadata CSV: Metadata and annotations per speaker and line.
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- Audio_files/: Folder containing all WAV files.
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- Speaker_Profiles: Included in the metadata csv.

Sample Fields:

text: Emotionally rich sentence

emotion_label: Primary labeled emotion (e.g., joy, anger, fear)

audio_file_path: Path to the voice file

gender, age_range, region, native_language: Speaker metadata

For more details, refer to the embedded notebook.

**Additional Insights**
Inspired by the DAIR Twitter Emotions dataset, this voice corpus bridges the gap between text-based emotion classification and real-world speech-based emotion understanding. The data was ethically collected and fully consented, ensuring responsible AI development. The balance across emotions and speaker demographics enables equitable performance across diverse populations.

For more details, please feel free to reach out to us at [e-mail]

**概述**
情感理解是以人为中心的人工智能（human-centered AI）的核心支柱。本数据集包含来自500名真实说话者的5万条经过情感标注的合成语音录音，可为情感语音检测与建模任务提供高质量的音频基准测试集，可应用于语音助手、心理健康监测、共情式人工智能（empathetic AI）以及带有情感细微差别的语音处理等多个场景。

**应用场景**
- 情感识别训练：构建或评估可对具有真实世界多样性的情感语调进行分类的语音模型。
- 表现力文本转语音（Expressive Text-to-Speech, TTS）：训练语音合成模型生成带有富有表现力的情感线索的语音。
- 心理健康与福祉应用：通过实时语音线索识别痛苦情绪或情绪波动的迹象。
- 无障碍应用与社交机器人技术：实现更具情感感知能力的人机交互。

**产品详情**
- 元数据CSV文件：包含每位说话者及每条语音的元数据与标注信息。
- Audio_files/ 文件夹：存储所有WAV格式音频文件。
- 说话者档案（Speaker_Profiles）：包含于元数据CSV文件中。

示例字段：
- text：富含情感的语句
- emotion_label：主要标注的情感类型（如喜悦、愤怒、恐惧）
- audio_file_path：语音文件的存储路径
- gender, age_range, region, native_language：说话者元数据

如需了解更多细节，请参阅内嵌笔记本。

**补充说明**
本语音语料库的设计灵感源自DAIR Twitter情感数据集（DAIR Twitter Emotions dataset），填补了基于文本的情感分类与基于真实世界语音的情感理解之间的鸿沟。该数据集的采集遵循伦理规范且已获得完全知情同意，可为负责任的人工智能开发提供保障。数据集在情感类别与说话者人口统计学特征方面分布均衡，可确保在不同人群中实现公平的模型性能。

如需获取更多详情，请通过[e-mail]联系我们。

搜集汇总

背景与挑战

背景概述

该数据集包含50,000条来自500位真实说话者的合成语音记录，带有情感标注，适用于语音情感识别、表达性语音合成等AI应用。数据包含音频文件、元数据及说话者人口统计信息，覆盖美加地区，并遵循伦理收集标准。