Speakers & ABSTRACTS

Speakers & abstracts

The following speakers will give a presentation at the third SpeechTechday, Feb 2, 2026.

Will be further updated

Session I Keynote

David van Leeuwen  (Radboud University, Spraaklab)

Title: Sound or silence? 

Abstract: In this talk I will present my views and experiences in speech technology in relation to this Speech Tech Day's theme.  I will do this along the lines of several counterparts: science vs technology, academia vs industry, difficult vs solved problems, speech vs non-speech, speaker vs speech recognition, engineered vs learned features, LLM vs human, and past vs future knowledge. 

Session II

Aki Kunikoshi (ReadSpeaker)

Title: A Comparative Look at Industry and Academic Research

Abstract: Research conducted in companies and research carried out in universities may look similar at first glance, yet they differ in fundamental ways. Their goals, the processes through which ideas are developed, and the users they ultimately serve are shaped by distinct missions. Industrial research is driven by real world applications, product timelines, and the needs of customers and markets. Academic research, by contrast, is guided by curiosity, theory building, and the pursuit of new knowledge, often without immediate commercial constraints.

Drawing on my experience working at ReadSpeaker while remaining actively involved in the academic community, I will explore these contrasts through concrete examples. Having moved between both environments, I have seen how the same technical idea can evolve differently depending on its purpose.

In this talk, I will highlight what each environment values, how decisions are made, and how researchers navigate expectations, collaboration, and impact. By comparing these two worlds, I hope to offer insights that help participants understand where their interests and strengths may align, and how industry and academia can learn from one another to foster meaningful innovation.

Tessel Wisman (Juvoly)

Title: Project FRIS: Creating a Multi-Speaker, Multi-Dialect Frisian Speech Dataset for Robust ASR

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Martijn Bauer (Autoscriber)

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Roeland Ordelman & Xiyuan Gao

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Session III

Sil Aarts (U Maastricht)

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Lottie Stipdonk (Erasmus MC)

Title: Development of a child-friendly, clinical tool to objectively measure speech motor control in children who stutter

Abstract: Stuttering is a complex neurodevelopmental condition for which the biological cause remains unknown. While most children recover within 2–3 years after onset, approximately 25% develop persistent stuttering into adulthood, and early prediction is still not possible. This project therefore focuses on developing an objective, non invasive assessment tool targeting a proposed core underlying skill in stuttering: speech motor skill (SMS). SMS refers to the ability to coordinate the jaw, tongue, lips, and laryngeal system to produce stable speech movements. Reduced SMS leads to increased variability and instability in speech, making breakdowns (stutters) more likely. 

Because current clinical assessment relies largely on perceptual judgments with limited reliability, we aim to develop an instrument based alternative. In the ongoing OSMOS study, participating children (4–10 years) produce 5–8 repetitions of 30 nonwords varying in phonetic complexity. Acoustic analyses quantify within item variability across repetitions as an index of speech motor control. We examine group differences between children who stutter, children with a history of stuttering, and typically developing peers, as well as variability within the stuttering population. Complementary video based lip movement analyses capture potential compensation strategies (e.g. larger articulatory movements) and their impact on SMS. 

Following validation, this child friendly tool will provide clinicians with objective markers to support prognosis and treatment decisions, representing a substantial advance over current subjective clinical measures.

Thomas Wildschut (RUG)

Title: Speech technology in the classroom: using prosodic speech analysis to optimize inclusive adaptive fact learning systems

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