Lotte Weerts
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- Lab member: 2016-2021
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PhD thesis
Features of hearing: applications of machine learning to uncover the building blocks of hearing
Lotte Weerts was a PhD student in the Neurotechnology CDT, working on a combined information theory and machine learning approach to understanding the auditory system. She was jointly supervised by Claudia Clopath. After her PhD she moved to DeepMind.
Software
Python package for psychophysical tests of automatic speech recognition systems.
Videos
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The Psychometrics of Automatic Speech RecognitionTalk / 2022
Talk on applying psychometric testing to automatic speech recognition systems.
Publications
Note that only publications as part of the Neural Reckoning group are included here (see external publications below for full list).
2022
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Weerts L, Rosen S, Clopath C, Goodman DFM
The Psychometrics of Automatic Speech Recognition.
Preprint
2021
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Weerts L (2021)
Features of hearing: applications of machine learning to uncover the building blocks of hearing.
PhD thesis, Imperial College London
2019
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Weerts L, Clopath C, Goodman DFM (2019)
A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features.
Cognitive Computational Neuroscience - + 1 conference paper
External publications
This is a short preview of the publications from other sources (ORCID, Semantic Scholar). Note that publications from work done outside the Neural Reckoning group are included in this list.
2023
2021
2019
- L. Weerts, C. Clopath, Dan F. M. Goodman (2019)
A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features
2019 Conference on Cognitive Computational Neuroscience
2015
2014