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-21275@www.clsp.jhu.edu DTSTAMP:20240328T180558Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\n\n\n\nAutomatic discovery of phone or word-like units is one of the core objectives in zero-resource speech processing. Recent attempts employ contrastive predictive coding (CPC)\, where the model lear ns representations by predicting the next frame given past context. Howeve r\, CPC only looks at the audio signal’s structure at the frame level. The speech structure exists beyond frame-level\, i.e.\, at phone level or eve n higher. We propose a segmental contrastive predictive coding (SCPC) fram ework to learn from the signal structure at both the frame and phone level s.\n\nSCPC is a hierarchical model with three stages trained in an end-to- end manner. In the first stage\, the model predicts future feature frames and extracts frame-level representation from the raw waveform. In the seco nd stage\, a differentiable boundary detector finds variable-length segmen ts. In the last stage\, the model predicts future segments to learn segmen t representations. Experiments show that our model outperforms existing ph one and word segmentation methods on TIMIT and Buckeye datasets. DTSTART;TZID=America/New_York:20220211T120000 DTEND;TZID=America/New_York:20220211T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Saurabhchand Bhati “Segmental Contrastive Predict ive Coding for Unsupervised Acoustic Segmentation” URL:https://www.clsp.jhu.edu/events/student-seminar-saurabhchand-bhati/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\n\n\n\n\nAutomatic discovery of phone or word-like units is one of the core objectives in zero-resource speech processing. Recent attempts employ contrastive predictive coding (CPC)\, where the model learns repre sentations by predicting the next frame given past context. However\, CPC only looks at the audio signal’s structure at the frame level. The speech structure exists beyond frame-level\, i.e.\, at phone level or even higher . We propose a segmental contrastive predictive coding (SCPC) framework to learn from the signal structure at both the frame and phone levels.\n\n\nSCPC is a hierarchical mode l with three stages trained in an end-to-end manner. In the first stage\, the model predicts future feature frames and extracts frame-level represen tation from the raw waveform. In the second stage\, a differentiable bound ary detector finds variable-length segments. In the last stage\, the model predicts future segments to learn segment representations. Experiments sh ow that our model outperforms existing phone and word segmentation methods on TIMIT and Buckeye datasets.
Abstr act
\nModel robustness and spuri ous correlations have received increasing attention in the NLP community\, both in methods and evaluation. The term “spurious correlation” is overlo aded though and can refer to any undesirable shortcuts learned by the mode l\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contra st\, they carry important information that’s needed to make predictions by humans. In this talk\, we argue that it is more productive to characteriz e features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation\, l earning\, and evaluation.
\nBiography
\nHe He is an Assistant Professor in the Department of Computer Science and the C enter for Data Science at New York University. She obtained her PhD in Com puter Science at the University of Maryland\, College Park. Before joining NYU\, she spent a year at AWS AI and was a post-doc at Stanford Universit y before that. She is interested in building robust and trustworthy NLP sy stems in human-centered settings. Her recent research focus includes robus t language understanding\, collaborative text generation\, and understandi ng capabilities and issues of large language models.
\n