Spoken Language Understanding (SLU) is crucial for enabling natural voice interactions with modern devices. However, traditional supervised models fail to generalize to new domains due to two key challenges: the prohibitive cost of data annotation and the inherent difficulty of transferring domain-specific intents. While the rise of Large Language Models (LLMs) offers a promising solution through zero-shot inference, the zero-shot SLU capabilities of emerging speech-enabled LLMs have remained largely unexplored. To address this gap, this paper provides the first comprehensive assessment, focusing on intent classification (IC), the first key sub-task of SLU, across 13 languages. We systematically evaluate a range of architectures, including cascaded, end-to-end, and hybrid systems for zero-shot SLU. Our analysis identifies the hybrid approach as the most effective architectural design for end-to-end SLU, and assesses multilingual transfer capabilities. The findings offer a detailed map of the challenges and opportunities, highlighting which models and settings are most promising for zero-shot SLU.
Towards zero-shot SLU: an empirical study of competing architectural paradigms
| Beomseok Lee, Marco Gaido, Ioan Calapodescu, Laurent Besacier, Matteo Negri |
| The International Conference on Spoken Language Translation (IWSLT), San Diego, CA, USA, 3-4 July, 2026 |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
This web site uses cookies for the site search, to display videos and for aggregate site analytics.
Learn more about these cookies in our privacy notice.
Cookie settings
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.