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.