BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21057@www.clsp.jhu.edu DTSTAMP:20240329T071928Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab. \nBiography\nHeidi is a Senior Lecturer (associate professor) in Computer Science at the University of Sheffield\, United Kingdom. Her research inte rests are on the application of AI-based voice technologies to healthcare. In particular\, the detection and monitoring of people’s physical and men tal health including verbal and non-verbal traits for expressions of emoti on\, anxiety\, depression and neurodegenerative conditions in e.g.\, thera peutic or diagnostic settings. DTSTART;TZID=America/New_York:20211119T120000 DTEND;TZID=America/New_York:20211119T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Heidi Christensen (University of Sheffield\, UK) Virtual Seminar “A utomated Processing of Pathological Speech: Recent Work and Ongoing Challe nges” URL:https://www.clsp.jhu.edu/events/heidi-christensen-university-of-sheffie ld-uk-virtual-seminar-automated-processing-of-pathological-speech-recent-w ork-and-ongoing-challenges/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nThis talk will outline the major challenging in porti ng mainstream speech technology to the domain of clinical applications\; i n particular\, the need for personalised systems\, the challenge of workin g in an inherently sparse data domain and developing meaningful collaborat ions with all stakeholders. The talk will give an overview of recent state -of-the-art research from current projects including in the areas of recog nition of disordered speech\, automatic processing of conversations and th e automatic detection and tracking of paralinguistic information at the Un iversity of Sheffield (UK)’s Speech and Hearing (SPandH) & Healthcare lab.
\nBiography
\nHeidi is a Senior Lecturer (as sociate professor) in Computer Science at the University of Sheffield\, Un ited Kingdom. Her research interests are on the application of AI-based vo ice technologies to healthcare. In particular\, the detection and monitori ng of people’s physical and mental health including verbal and non-verbal traits for expressions of emotion\, anxiety\, depression and neurodegenera tive conditions in e.g.\, therapeutic or diagnostic settings.
\n X-TAGS;LANGUAGE=en-US:2021\,Christensen\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-21497@www.clsp.jhu.edu DTSTAMP:20240329T071928Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nWhile the “deep learning tsunami” continues to define the state of the art in speech and language processing\, finite-state tra nsducer grammars developed by linguists and engineers are still widely use d in industrial\, highly-multilingual settings\, particularly for symbolic \, “front-end” speech applications. In this talk\, I will first briefly re view the current state of the OpenFst and OpenGrm finite-state transducer libraries. I then review two “late-breaking” algorithms found in these lib raries. The first is a heuristic but highly-effective general-purpose opti mization routine for weighted transducers. The second is an algorithm for computing the single shortest string of non-deterministic weighted accepto rs which lack certain properties required by classic shortest-path algorit hms. I will then illustrate how the OpenGrm tools can be used to induce a finite-state string-to-string transduction model known as a pair n-gram mo del. This model has been applied to grapheme-to-phoneme conversion\, loanw ord detection\, abbreviation expansion\, and back-transliteration\, among other tasks.\nBiography\nKyle Gorman is an assistant professor of linguist ics at the Graduate Center\, City University of New York\, and director of the master’s program in computational linguistics\; he is also a software engineer in the speech and language algorithms group at Google. With Rich ard Sproat\, he is the coauthor of Finite-State Text Processing (Morgan & Claypool\, 2021) and the creator of Pynini\, a finite-state text processin g library for Python. He has also published on statistical methods for com paring computational models\, text normalization\, grapheme-to-phoneme con version\, and morphological analysis\, as well as many topics in linguisti c theory. DTSTART;TZID=America/New_York:20220401T120000 DTEND;TZID=America/New_York:20220401T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Kyle Gorman (City University of New York) ” Weighted Finite-State T ransducers: The Later Years” URL:https://www.clsp.jhu.edu/events/kyle-gorman-city-university-of-new-york -weighted-finite-state-transducers-the-later-years/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nWhile the “deep learning tsunami” continues to define the state of the art in speech and language processing\, finite-state tra nsducer grammars developed by linguists and engineers are still widely use d in industrial\, highly-multilingual settings\, particularly for symbolic \, “front-end” speech applications. In this talk\, I will first briefly re view the current state of the OpenFst and OpenGrm finite-state transducer libraries. I then review two “late-breaking” algorithms found in these lib raries. The first is a heuristic but highly-effective general-purpose opti mization routine for weighted transducers. The second is an algorithm for computing the single shortest string of non-deterministic weighted accepto rs which lack certain properties required by classic shortest-path algorit hms. I will then illustrate how the OpenGrm tools can be used to induce a finite-state string-to-string transduction model known as a pair n-gram mo del. This model has been applied to grapheme-to-phoneme conversion\, loanw ord detection\, abbreviation expansion\, and back-transliteration\, among other tasks.
\nBiography
\nKyle Gorman is an assistant professor of linguistics at the Graduate Center\, City Universit y of New York\, and director of the master’s program in computational ling uistics\; he is also a software engineer in the speech and language algori thms group at Google. With Richard Sproat\, he is the coauthor of Finit e-State Text Processing (Morgan & Claypool\, 2021) and the creator of Pynini\, a finite-state text processing library for Python. He has also pu blished on statistical methods for comparing computational models\, text n ormalization\, grapheme-to-phoneme conversion\, and morphological analysis \, as well as many topics in linguistic theory.
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