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Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition
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In: EUSIPCO 2020 - 28th European Signal Processing Conference ; https://hal.inria.fr/hal-02355669 ; EUSIPCO 2020 - 28th European Signal Processing Conference, Jan 2021, Amsterdam / Virtual, Netherlands. ⟨10.23919/Eusipco47968.2020.9287541⟩ ; https://eusipco2020.org/ (2021)
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A comparative study of different features for efficient automatic hate speech detection
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In: IPrA 2021 - 17th International Pragmatics Conference ; https://hal.archives-ouvertes.fr/hal-03115781 ; IPrA 2021 - 17th International Pragmatics Conference, Jun 2021, Winterthur, Switzerland (2021)
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Abstract:
International audience ; Commonly, Hate Speech (HS) is defined as any communication that disparages a person or agroup on the basis of some characteristic (race, colour, ethnicity, gender, sexual orientation, na-tionality, etc. (Nockeby, 2000)). Due to the massive activities of user-generator on social networks(around 500 million tweets per day) Hate Speech is continuously increasing on the web.Recent initiatives, such as SemEval2019 shared task 5 Hateval2019 (Basile et al., 2019) contri-bute to the development of automatic hate speech detection systems (HSD) by making availableannotated hateful corpus. We focus our research on automatic classification of hateful tweets,which are the first sub-task of Hateval2019. The best Hateval2019 HSD system was FERMI (In-durthi et al., 2019) with 65.1 % macro-F1 score on the test corpus. This system used sentenceembeddings, Universal Sentence Encoder (USE) (Cer et al., 2018) as input of a Support VectorMachine classifier.In this article, we study the impact of different features on an HSD system. We use deep neu-ral network (DNN) based classifier with USE. We investigate the word level features, such aslexicon of hateful words (HFW), Part of Speech (POS), uppercase letters (UP), punctuationmarks (PUNCT), the ratio of the number of times a word appears in hateful tweets comparedto the total number of times that word appears (RatioHW) ; and the emojis (EMO). We think thatthese features are relevant because they carry feelings. For instance, cases (UP) and punctuations(PUNCT) can carry the intonation of the tweets and can be used to express a hateful content. ForHFW features, we tag each word of tweets as hateful or not using the Hatebase lexicon (Hate-base.org) and we associate a binary value to each word. For POS features, we use twpipe (Liu etal., 2018) for tagging the words and this information is coded as an one-hot vector. For emojis,we generate an embedding vector using emoji2vec tools (Eisner et al., 2016). The input of ourneural network consists of the USE vector and our additional features. We used convolutionalneural networks (CNN) as binary classifier. We performed the experiments on the HateEval2019corpus to study the influence of each proposed feature. Our baseline system without proposedfeatures achieves 65.7% of macro-F1 score on the test corpus. Surprisingly, HFW degrades thesystem performance and decreases the macro-F1 by 14 points compared to the baseline. Thiscan be due to the fact that some words are hateful only in a particular context. UP, RatioHWand PUNCT slightly degrade the baseline system. The POS features do not change the baselinesystem result and so are probably not correlated to the hate speech. The best result is obtainedusing EMO features with 66.0% of macro-F1. EMOs are largely used to transmit emotions. Inour system,they are modeled by a specific embedding vector. USE does not take into account theemojis. Therefore, EMOs give additional information to USE about the hateful content of tweets.
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Keyword:
[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [INFO]Computer Science [cs]
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URL: https://hal.archives-ouvertes.fr/hal-03115781/file/CFP___Offensive_language_on_social_media___International_Pragmatics_Conference_panel.pdf
https://hal.archives-ouvertes.fr/hal-03115781/document
https://hal.archives-ouvertes.fr/hal-03115781
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| 3 |
Multiword Expression Features for Automatic Hate Speech Detection
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In: NLDB 2021 - 26th International Conference on Natural Language & Information Systems ; https://hal.archives-ouvertes.fr/hal-03231047 ; NLDB 2021 - 26th International Conference on Natural Language & Information Systems, Jun 2021, Saarbrücken/Virtual, Germany ; http://nldb2021.sb.dfki.de/ (2021)
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BERT-based Semantic Model for Rescoring N-best Speech Recognition List
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In: INTERSPEECH 2021 ; https://hal.archives-ouvertes.fr/hal-03248881 ; INTERSPEECH 2021, Aug 2021, Brno, Czech Republic ; https://www.interspeech2021.org/ (2021)
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Improving Automatic Hate Speech Detection with Multiword Expression Features ...
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SLOGD: Speaker Location Guided Deflation Approach to Speech Separation
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In: ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing ; https://hal.inria.fr/hal-02355613 ; ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing, May 2020, Barcelona, Spain (2020)
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Introduction of semantic model to help speech recognition
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In: TSD 2020 - Twenty-third International Conference on Text, Speech and Dialogue ; https://hal.archives-ouvertes.fr/hal-02862245 ; TSD 2020 - Twenty-third International Conference on Text, Speech and Dialogue, Sep 2020, Brno, Czech Republic (2020)
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Introduction d’informations sémantiques dans un système de reconnaissance de la parole
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In: Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole ; https://hal.archives-ouvertes.fr/hal-02798559 ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 1 : Journées d'Études sur la Parole, 2020, Nancy, France. pp.362-369 (2020)
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RNN Language Model Estimation for Out-of-Vocabulary Words
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In: Lecture Notes in Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03054936 ; Lecture Notes in Artificial Intelligence, Springer, In press, 12598, ⟨10.1007/978-3-030-66527-2_15⟩ (2020)
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DNN-Based Semantic Model for Rescoring N-best Speech Recognition List ...
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| 11 |
Extractive Text-Based Summarization of Arabic videos: Issues, Approaches and Evaluations
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In: ICALP: International Conference on Arabic Language Processing ; https://hal.archives-ouvertes.fr/hal-02314238 ; ICALP: International Conference on Arabic Language Processing, Oct 2019, Nancy, France. pp.65-78, ⟨10.1007/978-3-030-32959-4_5⟩ (2019)
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A Fine-grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos
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In: ICALP: International Conference on Arabic Language Processing ; https://hal.archives-ouvertes.fr/hal-02314244 ; ICALP: International Conference on Arabic Language Processing, Oct 2019, Nancy, France. pp.49-61, ⟨10.1007/978-3-030-32959-4_4⟩ (2019)
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Machine Translation on a parallel Code-Switched Corpus
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In: Canadian AI 2019 - 32nd Conference on Canadian Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-02106010 ; Canadian AI 2019 - 32nd Conference on Canadian Artificial Intelligence, May 2019, Ontario, Canada (2019)
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Summarizing videos into a target language: Methodology, architectures and evaluation
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In: ISSN: 1064-1246 ; EISSN: 1875-8967 ; Journal of Intelligent and Fuzzy Systems ; https://hal.archives-ouvertes.fr/hal-02271287 ; Journal of Intelligent and Fuzzy Systems, IOS Press, 2019, 1, pp.1-12. ⟨10.3233/JIFS-179350⟩ (2019)
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| 15 |
Adaptation of speech recognition vocabularies for improved transcription of YouTube videos
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In: ISSN: 2351-8715 ; Journal of International Science and General Applications ; https://hal.archives-ouvertes.fr/hal-01873801 ; Journal of International Science and General Applications, ISGA, 2018, 1 (1), pp.1-9 ; http://journal-isga.ma/ (2018)
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| 16 |
Keyword-based speaker localization: Localizing a target speaker in a multi-speaker environment
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In: Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01817519 ; Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association, Sep 2018, Hyderabad, India (2018)
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Dynamic Extension of ASR Lexicon Using Wikipedia Data
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In: IEEE Workshop on Spoken and Language Technology (SLT) ; https://hal.archives-ouvertes.fr/hal-01874495 ; IEEE Workshop on Spoken and Language Technology (SLT), Dec 2018, Athènes, Greece (2018)
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A First Summarization System of a Video in a Target Language
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In: MISSI 2018 - 11th edition of the International Conference on Multimedia and Network Information Systems ; https://hal.archives-ouvertes.fr/hal-01819720 ; MISSI 2018 - 11th edition of the International Conference on Multimedia and Network Information Systems, Sep 2018, Wrocław, Poland. pp.1-12 (2018)
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