The INTERSPEECH 2009 emotion challenge
The INTERSPEECH 2009 emotion challenge
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DOI：
10.21437/interspeech.2009-103
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Björn Schuller;S. Steidl;A. Batliner
中科院分区：
文献类型：
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作者：
Björn Schuller;S. Steidl;A. Batliner
在过去的十年里，我们看到了大量关于从言语中识别情感的文献。然而，与相关的语音处理任务（如自动语音和说话人识别）相比，实际上没有标准化的语料库和测试条件来比较完全相同条件下的性能。相反，使用的多种评估策略——比如交叉验证或没有适当实例定义的百分比分割——会妨碍精确的再现性。此外，为了面对更现实的场景，社区迫切需要更多的自发和更少的原型数据。本次INTERSPEECH 2009情感挑战旨在弥合人类语音情感识别的优秀研究与低兼容性结果之间的差距。FAU Aibo情感语料库[1]作为明确定义的测试和训练分区的基础，结合扬声器独立性和不同的房间声学，在大多数现实环境中需要。本文介绍了两种流行的语音情感识别方法的挑战、语料库、特征和基准结果。索引术语：情感，挑战，特征类型，分类
The last decade has seen a substantial body of literature on the recognition of emotion from speech. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions. Instead a multiplicity of evaluation strategies employed – such as cross-validation or percentage splits without proper instance definition – prevents exact reproducibility. Further, in order to face more realistic scenarios, the community is in desperate need of more spontaneous and less prototypical data. This INTERSPEECH 2009 Emotion Challenge aims at bridging such gaps between excellent research on human emotion recognition from speech and low compatibility of results. The FAU Aibo Emotion Corpus [1] serves as basis with clearly defined test and training partitions incorporating speaker independence and different room acoustics as needed in most reallife settings. This paper introduces the challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech. Index Terms: emotion, challenge, feature types, classification