✨ Do self-supervised speech models learn to encode language-specific linguistic features from their training data, or only more language-general acoustic correlates?

At #Interspeech2025 we presented our new Wav2Vec2-NL model and SSL-NL evaluation dataset to test this!

📄 https://arxiv.org/abs/2506.00981

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We are living in a time (again) when not all researchers are free to travel to international conferences. Thanks to everybody who stepped in (maybe last-minute) to present work on behalf of the original authors who could not attend #interspeech2025!
What a wonderful theme for this year’s #Interspeech2025: “Fair and Inclusive Speech Science and Technology”. Curious to see how many contributions will address this issue.

Want to learn how to analyze the inner workings of speech processing models? 🔍

Check out the programme for our tutorial, taking place at this year's Interspeech conference in Rotterdam: https://interpretingdl.github.io/speech-interpretability-tutorial/

The schedule features presentations and interactive sessions with a great team of co-organizers: Charlotte Pouw, Gaofei Shen, Martijn Bentum, Tom Lentz, @hmohebbi, @wzuidema, @gchrupala (and me!). We look forward to seeing you there 😃

#SpeechTech #SpeechScience #Interspeech2025

Poeppel et al's #Interspeech2025 contribution: https://arxiv.org/pdf/2505.23509
A biologically-inspired sound decomposition into spectro-temporal modulation features outperforms DNNs in audio classification tasks.