arXiv:2605.28642v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated significant potential for speech-to-text translation (S2TT). However, existing deployment paradigms face critical challenges: pure on-device models suffer from resource constraints, while centralized cloud systems incur severe privacy risks and bandwidth bottlenecks by transmitting raw voice data. Furthermore, most models exhibit English-centric biases, restricting many-to-many translation scaling. In this paper, we propose Edge-cloud Speech Recognition and Translation (ESRT), a privacy-p
The proliferation of advanced AI models and increasing privacy concerns are driving the need for more efficient and secure edge-cloud inference strategies for large language models.
This development addresses critical challenges in AI deployment, balancing computational efficiency, user privacy, and bandwidth consumption, which are key bottlenecks for broader AI adoption.
The proposed Edge-cloud Speech Recognition and Translation (ESRT) system offers a more bandwidth-efficient and privacy-preserving architecture for speech-to-text translation as compared to purely cloud-based or on-device solutions.
- · Edge AI hardware manufacturers
- · Cloud service providers (hybrid solutions)
- · Users in bandwidth-constrained regions
- · AI developers focused on privacy
- · Purely on-device AI model developers
- · Purely centralized cloud AI service providers (without edge integration)
- · Bandwidth-intensive cloud-based translation services
Improved performance and broader accessibility of speech translation services due to reduced latency and enhanced privacy.
Accelerated development of other edge-cloud AI applications as this compute paradigm gains traction and becomes more standardized.
Enhanced digital inclusion for non-English speakers and those in regions with limited internet infrastructure, driving new economic opportunities.
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Read at arXiv cs.AI