Improving spontaneous children's emotion recognition by acoustic feature selection and feature-level fusion of acoustic and linguistic parameters
作者： Santiago Planet , Ignasi Iriondo
DOI: 10.1007/978-3-642-25020-0_12
关键词: Emotion recognition 、 Level fusion 、 Spontaneous speech 、 Linguistics 、 Feature selection 、 Feature vector 、 Merge (version control) 、 Computer science 、 Speech recognition 、 Pattern recognition 、 Artificial intelligence 、 AIBO
摘要: This paper presents an approach to improve emotion recognition from spontaneous speech. We used a wrapper method reduce acoustic set of features and feature-level fusion merge them with linguistic ones. The proposed system was evaluated the FAU Aibo Corpus. considered same that in Interspeech 2009 Emotion Challenge. main contribution this work is improvement, reduced features, results obtained Challenge combination best built selection 28 5 concatenation feature vectors original 389 parameters.
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