HENLO: Human voice Natural Language from On-demand media

The Human voice Natural Language from On-demand media (HENLO) dataset is a high-quality emotional speech dataset created to address the need for representative and realistic data in speech emotion recognition research. Unlike many existing datasets, which rely on simulated emotions performed by untrained speakers or directed participants, HENLO sources its data from professionally produced films and podcasts available on Media On-Demand (MOD). These audio samples feature trained actors employing the Stanislavski method, ensuring authentic emotional expressions that closely resemble real-life scenarios. The dataset prioritizes realism and quality, leveraging audio from films and podcasts produced by top-tier entertainment companies. Each clip undergoes rigorous mastering and scoring processes to ensure minimal environmental noise, making the dataset ideal for machine learning models requiring clean acoustic signals. This high-quality data enables researchers to extract and analyze features such as pitch, intonation, and rhythm with greater accuracy. Additionally, MOD offers unlimited access to a diverse collection of media, further enriching the dataset with varied emotional contexts. 

点播媒体人声自然语言（Human voice Natural Language from On-demand media，缩写HENLO）数据集是一款高质量情感语音数据集，旨在满足语音情感识别研究中对具有代表性与真实性数据的需求。与诸多依赖未经训练发言者或定向参与者演绎的模拟情感的现有数据集不同，HENLO的数据来源于点播媒体（Media On-Demand，缩写MOD）平台上的专业制作影视与播客内容。这些音频样本均由采用斯坦尼斯拉夫斯基表演法（Stanislavski method）的专业演员录制，确保情感表达真实自然，高度贴近现实生活场景。该数据集优先保障真实性与音质水准，选用顶级娱乐公司制作的影视与播客音频素材。每一段音频片段均经过严格的母带制作与评分流程，以尽可能降低环境噪声，使其非常适合需要纯净声学信号的机器学习模型使用。这类高质量数据可帮助研究人员更精准地提取与分析音高、语调、节奏等语音特征。此外，MOD平台可无限制获取多样化的媒体资源，进一步丰富了该数据集的情感场景多样性。