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End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
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In: ISBN: 978-1-6654-0019-0 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) ; https://hal.archives-ouvertes.fr/hal-03405970 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Sep 2021, Nara, Japan ; https://www.acii-conf.net/2021/ (2021)
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Corpus of Children Voices for Mid-level Markers and Affect Bursts Analysis
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In: Language Ressource and Evaluation Conference (LREC) ; https://hal.archives-ouvertes.fr/hal-01768827 ; Language Ressource and Evaluation Conference (LREC), 2012, Istanbul, Turkey (2012)
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Acoustic measures characterizing anger across corpora collected in artificial or natural context
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In: Speech Prosody ; https://hal.archives-ouvertes.fr/hal-01768783 ; Speech Prosody, 2010, Chicago, United States (2010)
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Whodunnit - Searching for the Most Important Feature Types Signalling Emotion-Related User States in Speech
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Batliner, Anton; Steidl, Stefan; Schuller, Björn; Seppi, Dino; Vogt, Thurid; Wagner, Johannes; Devillers, Laurence; Vidrascu, Laurence; Aharonson, Vered; Kessous, Loic; Amir, Noam
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In: ISSN: 0885-2308 ; EISSN: 1095-8363 ; Computer Speech and Language ; https://hal.archives-ouvertes.fr/hal-00661911 ; Computer Speech and Language, Elsevier, 2010, 25 (1), pp.4. ⟨10.1016/j.csl.2009.12.003⟩ (2010)
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Abstract:
International audience ; In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states - confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of 'most important' features which we try to interpret by discussing the impact of different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics.
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Keyword:
automatic classification; emotion; feature selection; feature types
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URL: https://hal.archives-ouvertes.fr/hal-00661911 https://hal.archives-ouvertes.fr/hal-00661911/document https://hal.archives-ouvertes.fr/hal-00661911/file/PEER_stage2_10.1016%252Fj.csl.2009.12.003.pdf https://doi.org/10.1016/j.csl.2009.12.003
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