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-21031@www.clsp.jhu.edu DTSTAMP:20240328T150000Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nMost people take for granted that when they speak\, t hey will be heard and understood. But for the millions who live with speec h impairments caused by physical or neurological conditions\, trying to co mmunicate with others can be difficult and lead to frustration. While ther e have been a great number of recent advances in Automatic Speech Recognit ion (ASR) technologies\, these interfaces can be inaccessible for those wi th speech impairments.\nIn this talk\, we will present Parrotron\, an end- to-end-trained speech-to-speech conversion model that maps an input spectr ogram directly to another spectrogram\, without utilizing any intermediate discrete representation. The system is also trained to emit words in addi tion to a spectrogram\, in parallel. We demonstrate that this model can be trained to normalize speech from any speaker regardless of accent\, pro sody\, and background noise\, into the voice of a single canonical target speaker with a fixed accent and consistent articulation and prosody. We fu rther show that this normalization model can be adapted to normalize highl y atypical speech from speakers with a variety of speech impairments (due to\, ALS\, Cerebral-Palsy\, Deafness\, Stroke\, Brain Injury\, etc.) \, r esulting in significant improvements in intelligibility and naturalness\, measured via a speech recognizer and listening tests. Finally\, demonstrat ing the utility of this model on other speech tasks\, we show that the sam e model architecture can be trained to perform a speech separation task.\n Dimitri will give a brief description of some key moments in development o f speech recognition algorithms that he was involved in and their applicat ions to YouTube closed captions\, Live Transcribe and wearable subtitles. \nFadi will then speak about the development of Parrotron.\nBiographies\nD imitri Kanevsky started his career at Google working on speech recognition algorithms. Prior to joining Google\, Dimitri was a Research staff member in the Speech Algorithms Department at IBM. Prior to IBM\, he worked at a number of centers for higher mathematics\, including Max Planck Institu te in Germany and the Institute for Advanced Studies in Princeton. He curr ently holds 295 US patents and was Master Inventor at IBM. MIT Technology Review recognized Dimitri conversational biometrics based security patent as one of five most influential patents for 2003. In 2012 Dimitri was hono red at the White House as a Champion of Change for his efforts to advance access to science\, technology\, engineering\, and math.\nFadi Biadsy is a senior staff research scientist at Google NY for the past ten years. He h as been exploring and leading multiple projects at Google\, including spee ch recognition\, speech conversion\, language modeling\, and semantic unde rstanding. He received his PhD from Columbia University in 2011. At Colum bia\, he researched a variety of speech and language processing projects i ncluding\, dialect and accent recognition\, speech recognition\, charismat ic speech and question answering. He holds a BSc and MSc in mathematics a nd computer science. He worked on handwriting recognition during his maste rs degree and he worked as a senior software developer for five years at D alet digital media systems building multimedia broadcasting systems. DTSTART;TZID=America/New_York:20211105T120000 DTEND;TZID=America/New_York:20211105T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fadi Biadsy and Dimitri Kanevsky (Google) “Speech Recognition: From Speaker Dependent to Speaker Independent to Full Personalization” “Parrot ron: A Unified E2E Speech-to Speech Conversion and ASR Model for Atypical Speech” URL:https://www.clsp.jhu.edu/events/fadi-biadsy-and-dimitri-kanevsky-google / X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nMost people take for granted that when they speak\, they will be heard and understood. But for the millions who live with speech impairments caused by physical or neurological condi tions\, trying to communicate with others can be difficult and lead to fru stration. While there have been a great number of recent advances in Autom atic Speech Recognition (ASR) technologies\, these interfaces can be inacc essible for those with speech impairments.
\nIn this talk\, we will present Parrotron\, an end-to-end-trained speech-to-sp eech conversion model that maps an input spectrogram directly to another s pectrogram\, without utilizing any intermediate discrete representation. T he system is also trained to emit words in addition to a spectrogram\, in parallel. We demonstrate that this model can be trained to normalize spe ech from any speaker regardless of accent\, prosody\, and background noise \, into the voice of a single canonical target speaker with a fixed accent and consistent articulation and prosody. We further show that this normal ization model can be adapted to normalize highly atypical speech from spea kers with a variety of speech impairments (due to\, ALS\, Cerebral-Palsy\, Deafness\, Stroke\, Brain Injury\, etc.) \, resulting in significant imp rovements in intelligibility and naturalness\, measured via a speech recog nizer and listening tests. Finally\, demonstrating the utility of this mod el on other speech tasks\, we show that the same model architecture can be trained to perform a speech separation task.
\nDimitri will give a brief description of some key moments in development o f speech recognition algorithms that he was involved in and their applicat ions to YouTube closed captions\, Live Transcribe and wearable subtitles.
\nFadi will then speak about the development of Parrotron.
\nBiographies
\nDimitri K anevsky started his career at Google working on speech recognitio n algorithms. Prior to joining Google\, Dimitri was a Research staff membe r in the Speech Algorithms Department at IBM. Prior to IBM\, he worked a t a number of centers for higher mathematics\, including Max Planck Instit ute in Germany and the Institute for Advanced Studies in Princeton. He cur rently holds 295 US patents and was Master Inventor at IBM. MIT Technology Review recognized Dimitri conversational biometrics based security patent as one of five most influential patents for 2003. In 2012 Dimitri was hon ored at the White House as a Champion of Change for his efforts to advance access to science\, technology\, engineering\, and math.
\nFadi Biadsy is a senior staff research scientist at Google NY for the past ten years. He has been exploring and leading multiple projects a t Google\, including speech recognition\, speech conversion\, language mod eling\, and semantic understanding. He received his PhD from Columbia Uni versity in 2011. At Columbia\, he researched a variety of speech and langu age processing projects including\, dialect and accent recognition\, speec h recognition\, charismatic speech and question answering. He holds a BSc and MSc in mathematics and computer science. He worked on handwriting rec ognition during his masters degree and he worked as a senior software deve loper for five years at Dalet digital media systems building multimedia br oadcasting systems.
\n X-TAGS;LANGUAGE=en-US:2021\,Biadsy and Kanevsky\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-23316@www.clsp.jhu.edu DTSTAMP:20240328T150000Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nUnderstanding the implications underlying a text is c ritical to assessing its impact\, in particular the social dynamics that m ay result from a reading of the text. This requires endowing artificial in telligence (AI) systems with pragmatic reasoning\, for example to correctl y conclude that the statement “Epidemics and cases of disease in the 21st century are “staged”” relates to unfounded conspiracy theories. In this ta lk\, I discuss how shortcomings in the ability of current AI systems to re ason about pragmatics present challenges to equitable detection of false o r harmful language. I demonstrate how these shortcomings can be addressed by imposing human-interpretable structure on deep learning architectures u sing insights from linguistics.In the first part of the talk\, I describe how adversarial text generation algorithms can be used to improve robustne ss of content moderation systems. I then introduce a pragmatic formalism f or reasoning about harmful implications conveyed by social media text. I s how how this pragmatic approach can be combined with generative neural lan guage models to uncover implications of news headlines. I also address the bottleneck to progress in text generation posed by gaps in evaluation of factuality. I conclude by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents.\n \nBiogr aphy\nSaadia Gabriel is a PhD candidate in the Paul G. Allen School of Com puter Science & Engineering at the University of Washington\, advised by P rof. Yejin Choi and Prof. Franziska Roesner. Her researchrevolves around n atural language processing and machine learning\, with a particular focus on building systems for understanding how social commonsense manifests in text (i.e. how do people typically behave in social scenarios)\, as well a s mitigating spread of false or harmful text (e.g. Covid-19 misinformation ). Her work has been covered by a wide range of media outlets like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nominatio n\, a 2019 IROS RoboCup best paper nomination and won a best paper award a t the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cu m laude from Mount Holyoke College in Computer Science and Mathematics.\n DTSTART;TZID=America/New_York:20230227T120000 DTEND;TZID=America/New_York:20230227T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Saadia Gabriel (University of Washington) “Socially Responsible and Factual Reasoning for Equitable AI Systems” URL:https://www.clsp.jhu.edu/events/saadia-gabriel-university-of-washington -socially-responsible-and-factual-reasoning-for-equitable-ai-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nUnderstanding the implications underlying a text is c
ritical to assessing its impact\, in particular the social dynamics that m
ay result from a reading of the text. This requires endowing artificial in
telligence (AI) systems with pragmatic reasoning\, for example to correctl
y conclude that the statement “Epidemics and cases of disease in the 21st
century are “staged”” relates to unfounded conspiracy theories. In this ta
lk\, I discuss how shortcomings in the ability of current AI systems to re
ason about pragmatics present challenges to equitable detection of false o
r harmful language. I demonstrate how these shortcomings can be addressed
by imposing human-interpretable structure on deep learning architectures u
sing insights from linguistics.
In the first part of the talk\, I describe how adversarial text gen
eration algorithms can be used to improve robustness of content moderation
systems. I then introduce a pragmatic formalism for reasoning about harmf
ul implications conveyed by social media text. I show how this pragmatic a
pproach can be combined with generative neural language models to uncover
implications of news headlines. I also address the bottleneck to progress
in text generation posed by gaps in evaluation of factuality. I conclude b
y showing how context-aware content moderation can be used to ensure safe
interactions with conversational agents.
\n
Biography
\nSaadia Gabr iel is a PhD candidate in the Paul G. Allen School of Computer Scie nce & Engineering at the University of Washington\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research re volves around natural language processing and machine learning\, with a pa rticular focus on building systems for understanding how social commonsens e manifests in text (i.e. how do people typically behave in social scenari os)\, as well as mitigating spread of false or harmful text (e.g. Covid-19 misinformation). Her work has been covered by a wide range of media outle ts like Forbes and TechCrunch. It has also received a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best paper nomination and won a bes t paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Holyoke College in Computer Sc ience and Mathematics.
\n\n X-TAGS;LANGUAGE=en-US:2023\,February\,Gabriel END:VEVENT END:VCALENDAR