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.

人声自然语言数据集（HENLO）是一部旨在满足语音情感识别研究对具有代表性且真实数据需求的高质量情感语音数据集。与众多依赖未经训练的演讲者或受指导的参与者模拟情感的现有数据集不同，HENLO的数据源于可在媒体点播（MOD）平台上获取的专业制作的电影和播客。这些音频样本由运用斯坦尼斯拉夫斯基方法的受过训练的演员演绎，确保了情感表达的真实性，与真实生活场景极为相似。该数据集以真实性和质量为首要目标，利用了顶级娱乐公司制作的电影和播客中的音频。每个片段都经过严格的制作和评分过程，以确保环境噪音降至最低，使数据集成为需要干净声学信号的机器学习模型的理想选择。这些高质量数据使研究人员能够以更高的准确性提取和分析诸如音高、语调和节奏等特征。此外，MOD平台提供对各种媒体资源的无限访问，进一步丰富了数据集，增添了多样化的情感背景。