Safeguards for Speech2Speech LLM-Assistants: A Case Study in Automotive Applications
Gregor Endler, Sebastian Kraus, Lukas Stappen
Why It Matters
What makes this one worth your time
Understanding the limitations of S2S guardrails is crucial for developing reliable and safe conversational AI systems in vehicles.
This study reveals significant latency issues in implementing safety measures for automotive speech assistants.
Summary
The paper evaluates two implementation approaches for safeguards in speech-to-speech conversational assistants in automotive applications, highlighting their limitations in terms of latency and technical challenges.
Key contributions
- Empirical evaluation of transcript-based and tool-based guardrails for S2S assistants.
- Identification of latency and technical challenges in implementing these safeguards.
Notable insights
- The paper identifies latency as a critical barrier to the deployment of safety measures in S2S systems.
- It highlights the non-deterministic behavior of tool calls as a significant technical impediment.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.21180v1 Announce Type: new Abstract: Recent advances have introduced speech-to-speech (S2S) conversational assistants capable of producing natural-sounding interactions, including non-verbal cues like tonality and mood. In the automotive domain, this enables intuitive and humanlike in-car dialogue experiences. However, integrating these end-to-end assistants limits architectural options for programmable domain-specific safeguards. This paper discusses two implementation approaches for S2S guardrails: transcript-based and tool-based. Through an empirical evaluation, we demonstrate that both strategies are insufficient for industrial deployment in most cases due to prohibitive latency (delaying each answer by 0 to 1.4 seconds even for computationally cheap checks) and technical impediments (like potentially non-deterministic tool call behavior). Finally, we outline open challenges for S2S guardrails in the automotive context.