arXiv 2509.097912025-09-11eess.AScs.SD

MSP-Podcast语料库

The MSP-Podcast Corpus

Language Technologies Institute, Carnegie Mellon University（语言技术研究所，卡内基梅隆大学）；Athenahealth；Accenture LLP；ARRAY Innovation；Bosch Center for Artificial Intelligence, Bosch Research（博世人工智能中心，博世研究）；Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas（埃里克·乔纳森工程与计算机科学学院，德克萨斯大学达拉斯分校）；Reality Labs at Meta Platforms, Inc.（Meta平台现实实验室）；Signal Analysis and Interpretation Laboratory (SAIL), Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California (USC)（信号分析与解释实验室（SAIL），明希德电子与计算机工程系，南加州大学（USC））

Carlos Busso, Reza Lotfian, Kusha Sridhar, Ali N. Salman, Wei-Cheng Lin, Lucas Goncalves, Srinivas Parthasarathy, Abinay Reddy Naini, Seong-Gyun Leem, Luz Marti… 展开作者

Carlos Busso, Reza Lotfian, Kusha Sridhar, Ali N. Salman, Wei-Cheng Lin, Lucas Goncalves, Srinivas Parthasarathy, Abinay Reddy Naini, Seong-Gyun Leem, Luz Martinez-Lucas, Huang-Cheng Chou, Pravin Mote

备注：: IEEE Transactions on Affective Computing submission

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文介绍了历经十年构建的MSP-Podcast语料库，包含超400小时音频，提供丰富情感标签与说话人识别，旨在解决现有数据库规模与多样性局限，以推进真实场景下的语音情感识别。

摘要：

大型高质量情感语音数据库的可用性，对于推进真实世界场景下的语音情感识别（SER）至关重要。然而，许多现有数据库在规模、情感平衡和说话人多样性方面存在局限。本研究描述了MSP-Podcast语料库，总结了为期十年的努力。该语料库包含来自各种音频分享网站的超过400小时的多样音频样本，所有样本均具有允许分发该语料库的公共许可证。我们为该语料库标注了丰富的情感标签，包括主要情感类别（单一主导情感）和次要情感类别（音频中感知到的多种情感），以及效价、唤醒度和支配度的情感属性。至少五名评分者标注这些情感标签。该语料库还包含大多数样本的说话人识别信息，以及整个语料库句子词汇内容的人工转录。数据收集协议包括一个机器学习驱动的流水线，用于选择情感多样的录音，确保跨说话人和环境的情感得到平衡且多样的表示。由此产生的数据库提供了一个全面、高质量的资源，更适合在实际的真实世界场景中推进SER系统。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。