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:20240328T105410Z 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-23586@www.clsp.jhu.edu DTSTAMP:20240328T105410Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230410T120000 DTEND;TZID=America/New_York:20230410T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Ruizhe Huang URL:https://www.clsp.jhu.edu/events/student-seminar-ruizhe-huang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Huang END:VEVENT BEGIN:VEVENT UID:ai1ec-23892@www.clsp.jhu.edu DTSTAMP:20240328T105410Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.\nBiography\nChien-Ming Huang is the John C . Malone Assistant Professor in the Department of Computer Science at the Johns Hopkins University. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier ven ues in HRI\, HCI\, and robotics including Science Robotics\, HRI\, CHI\, a nd CSCW. His research has received media coverage from MIT Technology Revi ew\, Tech Insider\, and Science Nation. Huang completed his postdoctoral t raining at Yale University and received his Ph.D. in Computer Science at t he University of Wisconsin–Madison. He is a recipient of the NSF CAREER aw ard. https://www.cs.jhu.edu/~cmhuang/ DTSTART;TZID=America/New_York:20230915T120000 DTEND;TZID=America/New_York:20230915T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Chien-Ming Huang (Johns Hopkins University) “Becoming Teammates: De signing Assistive\, Collaborative Machines” URL:https://www.clsp.jhu.edu/events/chien-ming-huang-johns-hopkins-universi ty/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.
\nBiography
\nChien-Ming Huang is the John C. Malone Assistant Professor in the Departm ent of Computer Science at the Johns Hopkins University. His research focu ses on designing interactive AI aimed to assist and collaborate with peopl e. He publishes in top-tier venues in HRI\, HCI\, and robotics including S cience Robotics\, HRI\, CHI\, and CSCW. His research has received media co verage from MIT Technology Review\, Tech Insider\, and Science Nation. Hua ng completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www .cs.jhu.edu/~cmhuang/
\n X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24479@www.clsp.jhu.edu DTSTAMP:20240328T105410Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nThe speech field is evolving to solve more challengin g scenarios\, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out there\, we present the UniX-Encoder. It’s a universal encoder designed for multiple tasks\, a nd worked with any microphone array\, in both solo and multi-talker enviro nments. Our research enhances previous multichannel speech processing effo rts in four key areas: 1) Adaptability: Contrasting traditional models con strained to certain microphone array configurations\, our encoder is unive rsally compatible. 2) MultiTask Capability: Beyond the single-task focus o f previous systems\, UniX-Encoder acts as a robust upstream model\, adeptl y extracting features for diverse tasks including ASR and speaker recognit ion. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to mod els that first beamform then process single-channels\, our encoder offers an end-to-end solution\, bypassing explicit beamforming or separation. To validate its effectiveness\, we tested the UniXEncoder on a synthetic mult i-channel dataset from the LibriSpeech corpus. Across tasks like speech re cognition and speaker diarization\, our encoder consistently outperformed combinations like the WavLM model with the BeamformIt frontend. DTSTART;TZID=America/New_York:20240311T200500 DTEND;TZID=America/New_York:20240311T210500 SEQUENCE:0 SUMMARY:Zili Huang (JHU) “Unix-Encoder: A Universal X-Channel Speech Encode r for Ad-Hoc Microphone Array Speech Processing” URL:https://www.clsp.jhu.edu/events/zili-huang-jhu-unix-encoder-a-universal -x-channel-speech-encoder-for-ad-hoc-microphone-array-speech-processing/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe speech field is evolving to solve more challenging scenarios\, such as multi-channel recordings wi th multiple simultaneous talkers. Given the many types of microphone setup s out there\, we present the UniX-Encoder. It’s a universal encoder design ed for multiple tasks\, and worked with any microphone array\, in both sol o and multi-talker environments. Our research enhances previous multichann el speech processing efforts in four key areas: 1) Adaptability: Contrasti ng traditional models constrained to certain microphone array configuratio ns\, our encoder is universally compatible. 2) MultiTask Capability: Beyon d the single-task focus of previous systems\, UniX-Encoder acts as a robus t upstream model\, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integr ation: In contrast to models that first beamform then process single-chann els\, our encoder offers an end-to-end solution\, bypassing explicit beamf orming or separation. To validate its effectiveness\, we tested the UniXEn coder on a synthetic multi-channel dataset from the LibriSpeech corpus. Ac ross tasks like speech recognition and speaker diarization\, our encoder c onsistently outperformed combinations like the WavLM model with the Beamfo rmIt frontend.
\n X-TAGS;LANGUAGE=en-US:2024\,Huang\,March END:VEVENT END:VCALENDAR