构建400小时高质量情感语音数据集，助力真实场景语音情绪识别
The MSP-Podcast Corpus
- 基于机器学习筛选情绪多样录音，确保跨说话人与环境的平衡
- 超400小时音频含主/次级情绪标签及愉悦度、唤醒度等属性标注
- 适合语音情绪识别、多模态情感计算研究者使用
大规模高质量情感语音数据库对推动实际场景中的语音情绪识别（SER）至关重要。然而，现有数据库普遍存在规模小、情绪分布不均、说话人多样性不足等问题。本文介绍为期十年的研究成果——MSP-Podcast语料库，包含来自多个音频分享网站超过400小时的多样化音频样本，所有内容均采用允许分发的通用许可证。我们为语料库添加了丰富的标注信息：包括主导情绪（单一主要情绪）和复合情绪（音频中感知到的多种情绪），以及愉悦度、唤醒度、支配度等情感属性。每条标注至少由五位评估者完成。大部分样本配有说话人标识，且整个语料库实现逐句人工转录。数据采集流程采用机器学习驱动的筛选机制，确保情绪多样性。该数据库为实际应用中的语音情绪识别系统提供了全面、高质量的资源。
原文摘要 · Abstract (English)
The availability of large, high-quality emotional speech databases is essential for advancing speech emotion recognition (SER) in real-world scenarios. However, many existing databases face limitations in size, emotional balance, and speaker diversity. This study describes the MSP-Podcast corpus, summarizing our ten-year effort. The corpus consists of over 400 hours of diverse audio samples from various audio-sharing websites, all of which have Common Licenses that permit the distribution of the corpus. We annotate the corpus with rich emotional labels, including primary (single dominant emotion) and secondary (multiple emotions perceived in the audio) emotional categories, as well as emotional attributes for valence, arousal, and dominance. At least five raters annotate these emotional labels. The corpus also has speaker identification for most samples, and human transcriptions of the lexical content of the sentences for the entire corpus. The data collection protocol includes a machine learning-driven pipeline for selecting emotionally diverse recordings, ensuring a balanced and varied representation of emotions across speakers and environments. The resulting database provides a comprehensive, high-quality resource, better suited for advancing SER systems in practical, real-world scenarios.
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