BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//LORIA - ECPv6.17.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:LORIA
X-ORIGINAL-URL:https://www.loria.fr
X-WR-CALDESC:Évènements pour LORIA
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20271031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260511T000000
DTEND;TZID=Europe/Paris:20260511T000000
DTSTAMP:20260504T123038Z
CREATED:20260504T123038Z
LAST-MODIFIED:20260504T123038Z
UID:29404-1778457600-1778457600@www.loria.fr
SUMMARY:Soutenance de thèse : Omar Anser
DESCRIPTION:Omar Anser soutiendra sa thèse le 11 mai 2026.
URL:https://www.loria.fr/event/soutenance-de-these-omar_anser/
CATEGORIES:Soutenance
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260522T093000
DTEND;TZID=Europe/Paris:20260522T123000
DTSTAMP:20260515T154810Z
CREATED:20260515T144609Z
LAST-MODIFIED:20260515T154810Z
UID:29472-1779442200-1779453000@www.loria.fr
SUMMARY:Soutenance de thèse : Nasser-Eddine Monir (équipe Multispeech)
DESCRIPTION:Le 22 mai 2026\, Nasser-Eddine Monir\, (Multispeech)\, soutiendra sa thèse intitulée \n« Phoneme-Level Evaluation and Training Losses for Multichannel Speech Enhancement »\n  \n\n\nSupervisors:\n\nRomain SERIZEL (Director)\, Full Professor\, Loria\, Université de Lorraine\nPaul MAGRON (Co-director)\, Research Scientist\, Inria Centre at Université de Lorraine\n\n\n\nReviewers:\n\nSimone GRAETZER\, Senior Research Scientist\, University of Salford\, UK\nRichard MARXER\, Full Professor\, LIS\, Université de Toulon\, France\n\n\n\nExaminers :\n\nJoël DUCOURNEAU (President)\, Full Professor\, LEMTA\, Université de Lorraine\nTobias MAY\, Associate Professor\, CAHR\, Danmarks Tekniske Universitet\, Denmark\nDorothée ARZOUNIAN\, Research Scientist\, Institut Pasteur\, France\n\n\n\n\nAbstract\n\nSpeech communication in complex acoustic environments remains a significant challenge\, particularly for hearing-impaired individuals and automatic speech recognition (ASR) systems. While deep learning has significantly advanced multichannel speech enhancement\, most existing frameworks rely on global\, utterance-level optimization criteria. This thesis addresses the limitations of such approaches by explicitly accounting for the structured and non-uniform nature of speech signals across time\, frequency\, and phoneme categories. \nThe first part of this work introduces a phoneme-level evaluation framework to characterize enhancement performance beyond traditional metrics such as the signal-to-distortion ratio. By analyzing performance across phonetic classes\, we demonstrate that global metrics are often dominated by high-energy\, stationary segments (such as vowels)\, effectively masking significant degradation in perceptually critical but low-energy transient units (such as plosives and fricatives). This analysis is further extended to investigate speaker-dependent variability\, revealing that speaker gender significantly influences enhancement behavior at the phoneme scale. \nBuilding on these findings\, the second part of the thesis focuses on the design of phoneme-aware training objectives. We first explore frequency-weighted loss functions that emphasize spectral regions and time–frequency bins dominated by interference. Results show that adaptive weighting leads to improved preservation of spectral cues\, particularly for consonants. Finally\, we propose a structured gated weighting framework that integrates speech presence\, local speech-noise competition\, and transient spectral structure into the optimization process. Evaluation across signal-level\, phonetic\, and ASR-based metrics confirms that these loss functions lead to an enhancement behavior that is more closely aligned with improved speech recognition performance and spectral reconstruction. \nOverall\, this thesis demonstrates that incorporating phonetic and spectro-temporal structure into both evaluation and training is essential for developing speech enhancement systems that better preserve the information relevant to human and automatic speech recognition. \nKeywords: Multichannel speech enhancement\, speech intelligibility\, hearing aids\, phoneme-based evaluation\, loss functions.
URL:https://www.loria.fr/event/soutenance-de-these-nasser-eddine-monir/
LOCATION:C005
CATEGORIES:Soutenance
END:VEVENT
END:VCALENDAR