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Construction of english-French multimodal affective conversational corpus from TV dramas
Novitasari S.a,b, Do Q.T.a, Sakti S.a, Lestari D.b, Nakamura S.a
a Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma-shi, Nara, Japan
b Department of Informatics, Bandung Institute of Technology, Bandung, Jawa Barat, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© LREC 2018 – 11th International Conference on Language Resources and Evaluation. All rights reserved.Recently, there has been an increase of interest in constructing corpora containing social-affective interactions. But the availability of multimodal, multilingual, and emotionally rich corpora remains limited. The tasks of recording and transcribing actual human-to-human affective conversations are also tedious and time-consuming. This paper describes construction of a multimodal affective conversational corpus based on TV dramas. The data contain parallel English-French languages in lexical, acoustic, and facial features. In addition, we annotated the part of the English data with speaker and emotion information. Our corpus can be utilized to develop and assess such tasks as speaker and emotion recognition, affective speech recognition and synthesis, linguistic, and paralinguistic speech-to-speech translation as well as a multimodal dialog system.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Affective conversation,Affective interaction,Corpus construction,Dialog systems,Emotion recognition,Paralinguistic,Parallel data,Speech-to-speech translation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Affective conversation,Corpus construction,Multimodal parallel data,Television dramas[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Part of this work was supported by JSPS KAKENHI Grant Numbers JP17H06101 and JP17K00237.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]