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-21023@www.clsp.jhu.edu DTSTAMP:20240329T064611Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSpeech data is notoriously difficult to work with due to a variety of codecs\, lengths of recordings\, and meta-data formats. W e present Lhotse\, a speech data representation library that draws upon le ssons learned from Kaldi speech recognition toolkit and brings its concept s into the modern deep learning ecosystem. Lhotse provides a common JSON d escription format with corresponding Python classes and data preparation r ecipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and cloud storage\, la zy and on-the-fly operations amongst other features. We introduce Cut and CutSet concepts\, which simplify common data wrangling tasks for audio and help incorporate acoustic context of speech utterances. Finally\, we show how Lhotse leverages PyTorch data API abstractions and adopts them to han dle speech data for deep learning.\nBiography\nPiotr Zelasko is an assista nt research scientist in the Center for Language and Speech Processing (CL SP) who specializes in automatic speech recognition (ASR) and spoken langu age understanding (SLU). His current research focuses on applying multilin gual and crosslingual speech recognition systems to categorize the phoneti c inventory of a previously unknown language and on improving defenses aga inst adversarial attacks on both speaker identification and automatic spee ch recognition systems. He is also addressing the question of how to struc ture a spontaneous conversation into high-level semantic units such as dia log acts or topics. Finally\, he is working on Lhotse + K2\, the next-gene ration speech processing research software ecosystem. Before joining Johns Hopkins\, Zelasko worked as a machine learning consultant for Avaya (2017 -2019)\, and as a machine learning engineer for Techmo (2015-2017). Zelask o received his PhD (2019) in electronics engineering\, as well as his mast er’s (2014) and undergraduate degrees (2013) in acoustic engineering from AGH University of Science and Technology in Kraków\, Poland. DTSTART;TZID=America/New_York:20211029T120000 DTEND;TZID=America/New_York:20211029T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore MD 21218 SEQUENCE:0 SUMMARY:Piotr Zelasko (CLSP at JHU) “Lhotse: a speech data representation l ibrary for the modern deep learning ecosystem” URL:https://www.clsp.jhu.edu/events/piotr-zelasko-clsp-at-jhu-lhotse-a-spee ch-data-representation-library-for-the-modern-deep-learning-ecosystem/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nSpeech data is notoriously difficult t o work with due to a variety of codecs\, lengths of recordings\, and meta- data formats. We present Lhotse\, a speech data representation library tha t draws upon lessons learned from Kaldi speech recognition toolkit and bri ngs its concepts into the modern deep learning ecosystem. Lhotse provides a common JSON description format with corresponding Python classes and dat a preparation recipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and clo ud storage\, lazy and on-the-fly operations amongst other features. We int roduce Cut and CutSet concepts\, which simplify common data wrangling task s for audio and help incorporate acoustic context of speech utterances. Fi nally\, we show how Lhotse leverages PyTorch data API abstractions and ado pts them to handle speech data for deep learning.
\nB iography
\nPiotr Zelasko is an assistant research scientist in the Center for Language and Speech Processing (CLSP) who specializes i n automatic speech recognition (ASR) and spoken language understanding (SL U). His current research focuses on applying multilingual and crosslingual speech recognition systems to categorize the phonetic inventory of a prev iously unknown language and on improving defenses against adversarial atta cks on both speaker identification and automatic speech recognition system s. He is also addressing the question of how to structure a spontaneous co nversation into high-level semantic units such as dialog acts or topics. F inally\, he is working on Lhotse + K2\, the next-generation speech process ing research software ecosystem. Before joining Johns Hopkins\, Zelasko wo rked as a machine learning consultant for Avaya (2017-2019)\, and as a mac hine learning engineer for Techmo (2015-2017). Zelasko received his PhD (2 019) in electronics engineering\, as well as his master’s (2014) and under graduate degrees (2013) in acoustic engineering from AGH University of Sci ence and Technology in Kraków\, Poland.
\n X-TAGS;LANGUAGE=en-US:2021\,October\,Zelasko END:VEVENT BEGIN:VEVENT UID:ai1ec-22375@www.clsp.jhu.edu DTSTAMP:20240329T064611Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nI will present our work on data augmentation using st yle transfer as a way to improve domain adaptation in sequence labeling ta sks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transform the labelled out of domain data into something that stylistically is more closely related t o the target data. Then we can train a model on a combination of the gener ated data and the smaller amount of in domain data to improve NER predicti on performance. I will show recent empirical results on these efforts.\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUAL (Research in Text Understanding and Analysis of Language) lab. The common thread among all these research problems is t he scarcity of labeled data.\nBiography\nThamar Solorio is a Professor of Computer Science at the University of Houston (UH). She holds graduate deg rees in Computer Science from the Instituto Nacional de Astrofísica\, Ópti ca y Electrónica\, in Puebla\, Mexico. Her research interests include info rmation extraction from social media data\, enabling technology for code-s witched data\, stylistic modeling of text\, and more recently multimodal a pproaches for online content understanding. She is the director and founde r of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attribution\, and recipient of the 2014 Emerging L eader ABIE Award in Honor of Denice Denton. She is currently serving a sec ond term as an elected board member of the North American Chapter of the A ssociation of Computational Linguistics and was PC co-chair for NAACL 2019 . She recently joined the team of Editors in Chief for the ACL Rolling Rev iew (ARR) system. Her research is currently funded by the NSF and by ADOBE . DTSTART;TZID=America/New_York:20220923T120000 DTEND;TZID=America/New_York:20220923T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Thamar Solorio (University of Houston) “Style Transfer for Data Aug mentation in Sequence Labeling Tasks” URL:https://www.clsp.jhu.edu/events/thamar-solorio-university-of-houston-st yle-transfer-for-data-augmentation-in-sequence-labeling-tasks/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nI will present our work on data a ugmentation using style transfer as a way to improve domain adaptation in sequence labeling tasks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transfo rm the labelled out of domain data into something that stylistically is mo re closely related to the target data. Then we can train a model on a comb ination of the generated data and the smaller amount of in domain data to improve NER prediction performance. I will show recent empirical results o n these efforts.
\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUA L (Research in Text Understanding and Analysis of Language) lab. The commo n thread among all these research problems is the scarcity of labeled data .
\nBiography
\nThamar Solorio is a Professor of Computer Science at the Univer sity of Houston (UH). She holds graduate degrees in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include information extraction from social media data\, enabling technology for code-switched data\, stylistic model ing of text\, and more recently multimodal approaches for online content u nderstanding. She is the director and founder of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attrib ution\, and recipient of the 2014 Emerging Leader ABIE Award in Honor of D enice Denton. She is currently serving a second term as an elected board m ember of the North American Chapter of the Association of Computational Li nguistics and was PC co-chair for NAACL 2019. She recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE.
\n X-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT END:VCALENDAR