Articulation
Articulation
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Articulation
Oral cancer speech corpus
Ultimate tensorflow install guide
UMCG Oral cancer speech corpus
Importance weighted autoencoders
Links which are interesting
Probabilistic AI Summary
About me
Exercise 1.1.1
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As part of the TAPAS Marie Curie ETN, I had the opportunity along with my research peers in the program to listen to a series of talks on unconscious bias and human-centred systems designs. While the talks were all from a different perspective, there were certainly common lessons that can be learned from these talks. In this article, I’m going to summarise these lessons and reflect a bit on the user-centred design part.
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This week NVIDIA’s new TTS model, Flowtron [4], has been released. I worked a lot with the Mellotron and its score parser for the Eurovision AI Song Contest (where we got third place, hooray!), so I was really interested in checking out this new model.
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One of the recurring themes in my PhD is the topic of naturalness of signals, in particular the naturalness of speech. It is challenging to define what we mean by naturalness – in fact this is the problem itself. Though, we could find a definition for it in “natural language”, it wouldn’t give us the mathematical formula for it.
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It was one of my mini PhD goals to train a full-fledged Tacotron 2 model, but I lacked the project goal and resources to do it. So my winter “vacation” came, meaning I could finally fiddle with the Mellotron model.
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It has been again a month since my last blog post, but here we are again. Recently, I started a collaboration in Delft so that we can discover even more aspects of pathological speech. For that, I had to do transcriptions of three hours of oral cancer speech, and I’ve learned many lessons which I think are interesting for other people and might be widely applicable.
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I’m currently working on making some of my code reproducible (meaning: producing the same exact results) on different platforms, and some of the lessons learned in the past few days hit so hard, that I thought it would be worth sharing these with other people.
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I spent two months, as part of my Marie Curie PhD program, at Oxford Wave Research, where I could learn about forensic speech technology from world-leading experts.
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Before Interspeech, we had a quick opportunity to showcase our results so far, and talk about data management and research ethics.
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Interspeech is drawing closer day by day, so I went through the planned schedule to cherry pick the titles that I found a bit more interesting than the others (from the perspective of pathological speech synthesis). I would like to use this opportunity to also go through the authors and show some impressive results.
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Short description of portfolio item number 1
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Short description of portfolio item number 2
Published in ICAASP 2020, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Halpern, van Son, van den Brekel (2020). "Towards taming oral cancer data in the wild: an explainable machine learning approach; ICASSP. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Speaker Odyssey 2020, 2020
This paper is about analysing a spoofing countermeasure under noisy conditions.
Recommended citation: Halpern, Kelly, van Son, Alexander (2020). " Analysing an embeddings-based spoofing countermeasure in noise; ODYSSEY 2020. 1(1). http://karkirowle.github.io/files/Odyssey2020_spoofingResNet_Halpern_et_al.pdf
Published in ITG Conference on Speech Communication | 29.09.2021 - 01.10.2021 | Kiel, 2021
An objectiveevaluation framework for pathologicalspeech synthesis
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There are two main AI approaches for synthesising singing (speech):
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.