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Upcoming Seminars

Sep

13

Fri

12:00 pm Luca Soldaini (Allen Institute f... @ Hackerman Hall B17

Luca Soldaini (Allen Institute f... @ Hackerman Hall B17

Sep 13 @ 12:00 pm – 1:15 pm

Abstract Recently, we have seen tremendous progress in the field of language models (LMs), with the release of numerous open models and closed API systems. However, fewer and fewer disclose how they are created: Which[...]

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Seminar Videos

Towards Meaning: The Tectogrammatical Sentence Representation - Jan Hajic - 2001

Presented by Henry Li Xinyuan
Abstract: We present a number of systems for the Voice Privacy Challenge,
including voice conversion based systems such as the
kNN-VC method and the WavLM voice Conversion method,
and text-to-speech (TTS) based systems including Whisper-
VITS. We found that while voice conversion systems better
preserve emotional content, they struggle to conceal speaker
identity in semi-white-box attack scenarios; conversely, TTS
methods perform better at anonymization and worse at emotion
preservation. Finally, we propose a random admixture system
which seeks to balance out the strengths and weaknesses of the
two category of systems, achieving a strong EER of over 40%
while maintaining UAR at a respectable 47%.

JHU HLTCOE Submission to the Voice Privacy Challenge 2024

Read more here: https://matthewfl.com/research

Declarative Programming via a Relational Algebra --- Matthew Francis Landau PhD defense

An Information State Approach to Collaborative Reference - Matthew Stone

Abstract
Be it in a restaurant or other reverberant and noisy environment, normal hearing listeners segregate multiple sources, usually strongly overlapping in frequency, well beyond capabilities expected by current beamforming approaches. What is it that we can learn from this common observation? As is now commonly accepted, the differing dynamical modulation patterns of the sources are key to these powers of separation. But until recently, the theoretical underpinnings for the notion of dynamical modulation patterns have been lacking. We have taken a previously loosely defined concept, called “modulation frequency analysis,” and developed a theory which allows for distortion-free separation (filtering) of multiple sound sources with differing dynamics. A key result is that previous assumptions of non-negative and real modulation are not sufficient and, instead, coherent separation approaches are needed to separate different modulation patterns. These results may have an impact in separation and representation of multiple simultaneous sound streams for speech, audio, hearing loss treatment, and underwater acoustic applications. This research also suggests exciting new and potentially important open theoretical questions for general nonstationary signal representations, extending beyond acoustic applications and potentially impacting other areas of engineering and physics.
Biography
Les Atlas received his M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 1979 and 1984, respectively. He joined the University of Washington in 1984, where he is currently a Professor of Electrical Engineering. His research is in digital signal processing, with specializations in acoustic analysis, time-frequency representations, and signal recognition and coding. Professor Atlas received a National Science Foundation Presidential Young Investigator Award and a 2004 Fulbright Senior Research Scholar Award. He was General Chair of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, Chair of the IEEE Signal Processing Society Technical Committee on Theory and Methods, and a member-at-large of the Signal Processing Society’s Board of Governors. He is a Fellow of the IEEE “for contributions to time-varying spectral analysis and acoustical signal processing.”

Acoustic Scene Analysis, Complex Modulations, and a New Form of Filtering – Les Atlas (UW) - 2008

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Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
3400 North Charles Street, Baltimore, MD 21218-2680

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Whiting School of Engineering
Johns Hopkins University

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