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:20240329T134927Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nMost p eople take for granted that when they speak\, they will be heard and under stood. But for the millions who live with speech impairments caused by phy sical or neurological conditions\, trying to communicate with others can b e difficult and lead to frustration. While there have been a great number of recent advances in Automatic Speech Recognition (ASR) technologies\, th ese interfaces can be inaccessible for those with speech impairments.
\nIn this talk\, we will present Parrotron\, an end -to-end-trained speech-to-speech conversion model that maps an input spect rogram directly to another spectrogram\, without utilizing any intermediat e discrete representation. The system is also trained to emit words in add ition to a spectrogram\, in parallel. We demonstrate that this model can be trained to normalize speech from any speaker regardless of accent\, pr osody\, and background noise\, into the voice of a single canonical target speaker with a fixed accent and consistent articulation and prosody. We f urther show that this normalization model can be adapted to normalize high ly atypical speech from speakers with a variety of speech impairments (due to\, ALS\, Cerebral-Palsy\, Deafness\, Stroke\, Brain Injury\, etc.) \, resulting in significant improvements in intelligibility and naturalness\, measured via a speech recognizer and listening tests. Finally\, demonstra ting the utility of this model on other speech tasks\, we show that the sa me model architecture can be trained to perform a speech separation task.< /p>\n
Dimitri will give a brief description of some key moments in development of speech recognition algorithms that he was in volved in and their applications to YouTube closed captions\, Live Transc ribe and wearable subtitles.
\nFadi will then sp eak about the development of Parrotron.
\nBiographies
\nDimitri Kanevsky started his career at Google working on speech recognition algorithms. Prior to joining Google\, Dimitr i 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 Institute in Germany and the Institute for Advanced Studies in Princeton. He currently holds 295 US patents and was Master Inv entor at IBM. MIT Technology Review recognized Dimitri conversational biom etrics based security patent as one of five most influential patents for 2 003. In 2012 Dimitri was honored at the White House as a Champion of Chang e for his efforts to advance access to science\, technology\, engineering\ , and math.
\nFadi Biadsy is a senior staff researc h scientist at Google NY for the past ten years. He has been exploring and leading multiple projects at Google\, including speech recognition\, spee ch conversion\, language modeling\, and semantic understanding. He receiv ed his PhD from Columbia University in 2011. At Columbia\, he researched a variety of speech and language processing projects including\, dialect an d accent recognition\, speech recognition\, charismatic speech and questio n answering. He holds a BSc and MSc in mathematics and computer science. He worked on handwriting recognition during his masters degree and he work ed as a senior software developer for five years at Dalet digital media sy stems 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-TAGS;LANGUAGE=en-US:2021\,Biadsy and Kanevsky\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-23316@www.clsp.jhu.edu DTSTAMP:20240329T134927Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nUnderstanding the implicat ions underlying a text is critical to assessing its impact\, in particular the social dynamics that may result from a reading of the text. This requ ires endowing artificial intelligence (AI) systems with pragmatic reasonin g\, for example to correctly conclude that the statement “Epidemics and ca ses of disease in the 21st century are “staged”” relates to unfounded cons piracy theories. In this talk\, I discuss how shortcomings in the ability of current AI systems to reason about pragmatics present challenges to equ itable detection of false or harmful language. I demonstrate how these sho rtcomings can be addressed by imposing human-interpretable structure on de ep learning architectures using insights from linguistics.
\n< p> In the first part of the talk\, I descri be how adversarial text generation algorithms can be used to improve robus tness of content moderation systems. I then introduce a pragmatic formalis m for reasoning about harmful implications conveyed by social media text. I show how this pragmatic approach 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 by showing how context-aware content moderation can be used to ensure safe interactions with conversational agents. \nBiography
\nSaadia Gabriel is a PhD candidate in the Paul G. Al len School of Computer Science & Engineering at the University of Washingt on\, advised by Prof. Yejin Choi and Prof. Franziska Roesner. Her research revolves around natural language processing and m achine learning\, with a particular focus on building systems for understa nding how social commonsense manifests in text (i.e. how do people typical ly behave in social scenarios)\, 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 outlets like Forbes and TechCrunch. It has also rece ived a 2019 ACL best short paper nomination\, a 2019 IROS RoboCup best pap er nomination and won a best paper award at the 2020 WeCNLP summit. Prior to her PhD\, Saadia received a BA summa cum laude from Mount Hol yoke College in Computer Science and Mathematics.
\nDTSTART;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-TAGS;LANGUAGE=en-US:2023\,February\,Gabriel END:VEVENT END:VCALENDAR