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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02415176 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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Challenges in Audio Processing of Terrorist-Related Data
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In: International Conference on Multimedia Modeling ; https://hal.archives-ouvertes.fr/hal-02387373 ; International Conference on Multimedia Modeling, Springer, Jan 2019, Thessaloniki, Greece (2019)
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An automatic study of lenition of intra-lexical intervocalic /bdg/ and coda -s in Peninsular vs America spanish
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In: Laboratory Phonology Conference ; https://hal.archives-ouvertes.fr/hal-01837162 ; Laboratory Phonology Conference, Jun 2018, Lisbonne, Portugal (2018)
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Connected speech in Romanian: Exploring sound change through an ASR system
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In: Production and perception mechanisms of sound change ; https://hal.archives-ouvertes.fr/hal-03127939 ; D. Recasens and F. Sánchez Miret (Eds.). Production and perception mechanisms of sound change, Lincom Europa, pp.129-143, 2018 (2018)
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Exploring Temporal Reduction in Dialectal Spanish: A Large-scale Study of Lenition of Voiced Stops and Coda-s
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In: Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02387395 ; Annual Conference of the International Speech Communication Association, ISCA, Sep 2018, Hyderabad, India (2018)
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Studying variation in Romanian: deletion of the definite article -l in continuous speech
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In: Linguistic Vanguard ; https://hal.archives-ouvertes.fr/hal-01837197 ; Linguistic Vanguard, 2018, 5 (1), 17p (2018)
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Phonetic variation and contrast neutralization patterns in Romanian fricatives accross different speaking styles
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In: Diversity and Speech Dynamics ; https://hal.archives-ouvertes.fr/hal-01837181 ; Diversity and Speech Dynamics, May 2017, Herrsching am Ammersee, Germany (2017)
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Marginal Contrast Among Romanian Vowels: Evidence from ASR and Functional Load
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In: Interspeech 2016 ; https://hal.archives-ouvertes.fr/hal-01453014 ; Interspeech 2016, ISCA, Sep 2016, San Francisco, United States. pp.2433 - 2437, ⟨10.21437/Interspeech.2016-762⟩ ; http://www.interspeech2016.org/ (2016)
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Réalisation phonétique et contraste phonologique marginal : une étude automatique des voyelles du roumain
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In: JEP 2016 ; https://hal.archives-ouvertes.fr/hal-01452974 ; JEP 2016, Aug 2016, Paris, France (2016)
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A phonologically weak contrast can induce phonetic overlap
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In: Laboratory Phonology Conference ; https://hal.archives-ouvertes.fr/hal-01837204 ; Laboratory Phonology Conference, Jul 2016, Ithaca, United States (2016)
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Analyzing linguistic variation in a Romanian speech corpus through ASR errors
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In: Laboratory Approaches to Romance Phonology ; https://hal.archives-ouvertes.fr/hal-01843421 ; Laboratory Approaches to Romance Phonology, Laboratoire Parole et Langage (UMR 6057), Aix-en-Provence, Sep 2014, Aix-en-Provence, France (2014)
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Exploring Pronunciation Variants for Romanian Speech-to-Text Transcription
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In: International Workshop on Spoken Languages Technologies for Under-resourced languages ; https://hal.archives-ouvertes.fr/hal-01843413 ; International Workshop on Spoken Languages Technologies for Under-resourced languages, May 2014, St. Petersburg, Russia (2014)
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Characterisation and identi cation of non-native French accents
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://halshs.archives-ouvertes.fr/halshs-00668927 ; Speech Communication, Elsevier : North-Holland, 2011, 53 (3), pp.292-310 (2011)
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Abstract:
International audience ; This paper focuses on foreign accent characterisation and identi cation in French. How many accents may a native French speaker recognise and which cues does (s)he use? Our interest concentrates on French productions stemming from speakers of six di erent mother tongues: Arabic, English, German, Italian, Portuguese and Spanish, also compared with native French speakers. Using automatic speech processing, our objective is to identify the most reliable acoustic cues distinguishing these accents, and to link these cues with human perception. We measured acoustic parameters such as duration and voicing for consonants, the rst two formant values for vowels, word- nal schwa-related prosodic features and the percentages of confusions obtained using automatic alignment including non-standard pronunciation variants. Machine learning techniques were used to select the most discriminant cues distinguishing different accents and to classify speakers according to their accents. The results obtained in automatic identi cation of the different linguistic origins under investigation compare favourably to perceptual data. Major identi ed accent-speci c cues include the devoicing of voiced stop consonants, /b/~/v/ and /s/~/z/ confusions, the "rolled r" and schwa fronting or raising. These cues can contribute to improve pronunciation modeling in automatic speech recognition of accented speech.
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Keyword:
[SHS.LANGUE]Humanities and Social Sciences/Linguistics; Automatic classi cation; Automatic speech alignment; Data mining techniques; Key words: Foreign accents; Non-native French; Perceptual experiments; Pronunciation variants
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URL: https://halshs.archives-ouvertes.fr/halshs-00668927 https://halshs.archives-ouvertes.fr/halshs-00668927/document https://halshs.archives-ouvertes.fr/halshs-00668927/file/SpeechCommForeignAccents2010.pdf
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Towards a multimedia knowledge-based agent with social competence and human interaction capabilities
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