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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 Acoustic Pairing of Original and Dubbed Voices in the Context of Video Game Localization Gresse, Adrien; Rouvier, Mickael; Dufour, Richard... In: Interspeech ; https://hal.archives-ouvertes.fr/hal-01572151 ; Interspeech, Aug 2017, Stockholm, Sweden. pp.2839-2843, ⟨10.21437/Interspeech.2017-1311⟩ ; http://www.isca-speech.org/archive/Interspeech_2017/abstracts/1311.html (2017) BASE Show details 2 Building a robust sentiment lexicon with (almost) no resource ... Rouvier, Mickael; Favre, Benoit. - : arXiv, 2016 BASE Show details 3 Multimodal embedding fusion for robust speaker role recognition in video broadcast Rouvier, Mickael; Delecraz, Sebastien; Favre, Benoit... In: Automatic Speech Recognition and Understanding ; https://hal.archives-ouvertes.fr/hal-01475413 ; Automatic Speech Recognition and Understanding, Dec 2015, Scottsdale, United States. pp.383 - 389, ⟨10.1109/ASRU.2015.7404820⟩ (2015) BASE Show details 4 Reranked aligners for interactive transcript correction Favre, Benoit; Rouvier, Mickael; Béchet, Frédéric In: ICASSP2014 - Speech and Language Processing (ICASSP2014 - SLTC) ; https://hal-amu.archives-ouvertes.fr/hal-01194237 ; ICASSP2014 - Speech and Language Processing (ICASSP2014 - SLTC), 2014, Florence, Italy (2014) BASE Show details 5 Semi-Supervised and Unsupervised Data Extraction Targeting Speakers: From Speaker Roles to Fame? Lailler, Carole; Dupuy, Grégor; Rouvier, Mickael... In: Proceedings of the First Workshop on Speech, Language and Audio in Multimedia (SLAM), ; Interspeech satellite workshop on Speech, Language and Audio in Multimedia (SLAM) ; https://hal.archives-ouvertes.fr/hal-01433450 ; Interspeech satellite workshop on Speech, Language and Audio in Multimedia (SLAM), 2013, Marseille, France (2013) BASE Show details 6 Modeling nuisance variabilities with factor analysis for GMM-based audio pattern classification Linarès, Georges; Matrouf, Driss; Verdet, Florian... In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 25 (2011) 3, 481-498 BLLDB OLC Linguistik Show details 7 Subspace Gaussian Mixture Models for vectorial HMM-states representation Bouallegue, Mohamed; Matrouf, Driss; Rouvier, Mickael... In: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) ; https://hal.archives-ouvertes.fr/hal-01318611 ; IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Dec 2011, Waikoloa, United States. ⟨10.1109/ASRU.2011.6163984⟩ (2011) BASE Show details 8 Query-Driven Strategy for On-the-Fly Term Spotting in Spontaneous Speech Rouvier, Mickael; Linarès, Georges; Lecouteux, Benjamin In: ISSN: 1687-4714 ; EISSN: 1687-4722 ; EURASIP Journal on Audio, Speech, and Music Processing ; https://hal.archives-ouvertes.fr/hal-01320220 ; EURASIP Journal on Audio, Speech, and Music Processing, SpringerOpen, 2010, ⟨10.1155/2010/326578⟩ (2010) BASE Show details 9 A Language-identification inspired method for spontaneous speech detection Rouvier, Mickael; Dufour, Richard; Linarès, Georges... In: INTERSPEECH ; https://hal.archives-ouvertes.fr/hal-01320176 ; INTERSPEECH, Sep 2010, Makuhari, Japan (2010) BASE Show details 10 Transcription-based video genre classification Oger, Stanislas; Rouvier, Mickael; Linarès, Georges In: IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-01319884 ; IEEE International Conference on Acoustics, Speech and Signal Processing , Mar 2010, Dallas, United States. ⟨10.1109/ICASSP.2010.5495042⟩ (2010) Abstract: International audience ; In this paper, we present a new method for video genre identification based on the linguistic content analysis. This approach relies on the analysis of the most frequent words in the video transcriptions provided by an automatic speech recognition system. Experiments are conducted on a corpus composed of cartoons, movies, news, commercials, documentary , sport and music. On this 7-genre identification task, the proposed transcription-based method obtains up to 80% of correct identification. Finally, this rate is increased to 95% by combining the proposed linguistic-level features with low-level acoustic features. Index Terms— video genre classification, audio-based video processing, linguistic feature extraction Keyword: [INFO]Computer Science [cs] URL: https://doi.org/10.1109/ICASSP.2010.5495042 https://hal.archives-ouvertes.fr/hal-01319884 BASE Hide details

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