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ShEMO: a large-scale validated database for Persian speech emotion detection

机译：ShEMO：用于波斯语语音情感检测的大规模验证数据库

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摘要

This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3h and 25min of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, the inter-annotator agreement is 64% which is interpreted as substantial agreement. We also present benchmark results based on common classification methods in speech emotion detection task. According to the experiments, support vector machine achieves the best results for both gender-independent (58.2%) and gender-dependent models (female=59.4%, male=57.6%). The ShEMO will be available for academic purposes free of charge to provide a baseline for further research on Persian emotional speech.
机译：本文介绍了一个大型的，经过验证的波斯语数据库，称为Sharif情感语音数据库（ShEMO）。该数据库包含3000种半自然语音，相当于从在线广播剧本中提取的语音数据的3小时25分钟。 ShEMO涵盖了87位以波斯语为母语的人的语音样本，涵盖了五种基本情绪，包括愤怒，恐惧，幸福，悲伤和惊奇以及中立状态。十二个注释器标记了语音的潜在情感状态，多数表决用于决定最终的标记。根据kappa度量，注释者之间的协议为64％，这被解释为实质协议。我们还提出了基于语音情感检测任务中常见分类方法的基准测试结果。根据实验，支持向量机在不依赖性别的模型（58.2％）和依赖性别的模型（女性= 59.4％，男性= 57.6％）上均达到最佳结果。 ShEMO将免费用于学术目的，为进一步研究波斯语情感言语提供基准。

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