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:20240328T165222Z 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-22403@www.clsp.jhu.edu DTSTAMP:20240328T165222Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nVoice conversion (VC) is a significant aspect of arti ficial intelligence. It is the study of how to convert one’s voice to soun d like that of another without changing the linguistic content. Voice conv ersion belongs to a general technical field of speech synthesis\, which co nverts text to speech or changes the properties of speech\, for example\, voice identity\, emotion\, and accents. Voice conversion involves multiple speech processing techniques\, such as speech analysis\, spectral convers ion\, prosody conversion\, speaker characterization\, and vocoding. With t he recent advances in theory and practice\, we are now able to produce hum an-like voice quality with high speaker similarity. In this talk\, Dr. Sis man will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the av ailable resources for expressive voice conversion research.\nBiography\nDr . Berrak Sisman (Member\, IEEE) received the Ph.D. degree in electrical an d computer engineering from National University of Singapore in 2020\, ful ly funded by A*STAR Graduate Academy under Singapore International Graduat e Award (SINGA). She is currently working as a tenure-track Assistant Prof essor at the Erik Jonsson School Department of Electrical and Computer Eng ineering at University of Texas at Dallas\, United States. Prior to joinin g UT Dallas\, she was a faculty member at Singapore University of Technolo gy and Design (2020-2022). She was a Postdoctoral Research Fellow at the N ational University of Singapore (2019-2020). She was an exchange doctoral student at the University of Edinburgh and a visiting scholar at The Centr e for Speech Technology Research (CSTR)\, University of Edinburgh (2019). She was a visiting researcher at RIKEN Advanced Intelligence Project in Ja pan (2018). Her research is focused on machine learning\, signal processin g\, emotion\, speech synthesis and voice conversion.\nDr. Sisman has serve d as the Area Chair at INTERSPEECH 2021\, INTERSPEECH 2022\, IEEE SLT 2022 and as the Publication Chair at ICASSP 2022. She has been elected as a me mber of the IEEE Speech and Language Processing Technical Committee (SLTC) in the area of Speech Synthesis for the term from January 2022 to Decembe r 2024. She plays leadership roles in conference organizations and active in technical committees. She has served as the General Coordinator of the Student Advisory Committee (SAC) of International Speech Communication Ass ociation (ISCA). DTSTART;TZID=America/New_York:20221104T120000 DTEND;TZID=America/New_York:20221104T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Berrak Sisman (University of Texas at Dallas) “Speech Synthesis and Voice Conversion: Machine Learning can Mimic Anyone’s Voice” URL:https://www.clsp.jhu.edu/events/berrak-sisman-university-of-texas-at-da llas/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nVoice conversion (VC) is a significant aspect of arti ficial intelligence. It is the study of how to convert one’s voice to soun d like that of another without changing the linguistic content. Voice conv ersion belongs to a general technical field of speech synthesis\, which co nverts text to speech or changes the properties of speech\, for example\, voice identity\, emotion\, and accents. Voice conversion involves multiple speech processing techniques\, such as speech analysis\, spectral convers ion\, prosody conversion\, speaker characterization\, and vocoding. With t he recent advances in theory and practice\, we are now able to produce hum an-like voice quality with high speaker similarity. In this talk\, Dr. Sis man will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the av ailable resources for expressive voice conversion research.
\nDr. Berrak Sisman (Member\, IEEE) received th e Ph.D. degree in electrical and computer engineering from National Univer sity of Singapore in 2020\, fully funded by A*STAR Graduate Academy under Singapore International Graduate Award (SINGA). She is currently working a s a tenure-track Assistant Professor at the Erik Jonsson School Department of Electrical and Computer Engineering at University of Texas at Dallas\, United States. Prior to joining UT Dallas\, she was a faculty member at S ingapore University of Technology and Design (2020-2022). She was a Postdo ctoral Research Fellow at the National University of Singapore (2019-2020) . She was an exchange doctoral student at the University of Edinburgh and a visiting scholar at The Centre for Speech Technology Research (CSTR)\, U niversity of Edinburgh (2019). She was a visiting researcher at RIKEN Adva nced Intelligence Project in Japan (2018). Her research is focused on mach ine learning\, signal processing\, emotion\, speech synthesis and voice co nversion.
\nDr. Sisman has served as the Area Chair at INTERSPEECH 2 021\, INTERSPEECH 2022\, IEEE SLT 2022 and as the Publication Chair at ICA SSP 2022. She has been elected as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) in the area of Speech Synthesis for the term from January 2022 to December 2024. She plays leadership roles i n conference organizations and active in technical committees. She has ser ved as the General Coordinator of the Student Advisory Committee (SAC) of International Speech Communication Association (ISCA).
\n X-TAGS;LANGUAGE=en-US:2022\,November\,Sisman END:VEVENT END:VCALENDAR