Building a naturalistic emotional speech corpus by retrieving expressive behaviors from existing speech corpora
Building a naturalistic emotional speech corpus by retrieving expressive behaviors from existing speech corpora
复制标题
通过从现有语音语料库中检索表达行为来构建自然情感语音语料库
DOI：
10.21437/interspeech.2014-60
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. Busso
中科院分区：
文献类型：
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作者：
Soroosh Mariooryad;Reza Lotfian;C. Busso
情感计算的一个关键因素是在自然对话中收集大量真实情感样本。通过电话记录自然互动是建立情感数据库的一种有吸引力的方法。然而，收集具有表达性反应的真实的对话数据是一项具有挑战性的任务，特别是如果记录要与社区共享（例如，隐私问题）。本研究探讨了一种新的方法，包括检索情绪反应，从现有的自发语音数据库收集一般的语音处理问题。虽然这些数据库中的大多数录音预计将具有非情感表达，但考虑到交互的自然性，对话的流程可能会导致我们旨在检索的对话伙伴的情感反应。我们使用IEMOCAP和SEMAINE数据库来构建情感检测系统。我们使用这些分类器来识别情感行为的费舍尔数据库，这是一个大型的会话语音语料库记录在电话上。对检索到的样本的主观评价表明，该计划的潜力，建立自然的情感语音数据库。索引词：情感识别，表达性语音，信息检索，情感数据库
A key element in affective computing is to have large corpora of genuine emotional samples collected during natural conversations. Recording natural interactions through telephone is an appealing approach to build emotional databases. However, collecting real conversational data with expressive reactions is a challenging task, especially if the recordings are to be shared with the community (e.g., privacy concerns). This study explores a novel approach consisting in retrieving emotional reactions from existing spontaneous speech databases collected for general speech processing problems. Although most of the recordings in these databases are expected to have non-emotional expressions, given the naturalness of the interactions, the flow of the conversation can lead to emotional responses from conversation partners which we aim to retrieve. We use the IEMOCAP and SEMAINE databases to build emotion detector systems. We use these classifiers to identify emotional behaviors from the FISHER database, which is a large conversational speech corpus recorded over the phone. Subjective evaluations over the retrieved samples demonstrate the potential of the proposed scheme to build naturalistic emotional speech database. Index Terms: emotion recognition, expressive speech, information retrieval, emotional databases