Safeguards for Speech2Speech LLM-Assistants: A Case Study in Automotive Applications
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 ...
| Authors | Gregor Endler, Sebastian Kraus, Lukas Stappen |
|---|---|
| Primary category | cs.AI |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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