Phoenix-VAD: detecting the end of speech in voice dialogues
An article published on September 4, 2026 presents an LLM-based model for semantic end-of-speech detection in full-duplex streaming voice interactions, using sliding-window training.
Source: Phoenix-VAD: Detecção Semântica de Fim de Fala em Streaming (arxiv.org). Text prepared with AI from this source.
What happened and what to do
The article presents Phoenix-VAD, an LLM-based model designed to detect semantically when a person has finished speaking during full-duplex streaming interactions. Its approach uses sliding-window training; the text does not report quantitative performance results.
For a company building voice interfaces, a practical response is to test end-of-speech detection on real or simulated streams and monitor latency, false interruptions, and turns that end too late. Implementation could include a private inference API, controlled event collection, and a dashboard for tracking these indicators before integrating the approach into dialogue flows.
How the consultancy can help
Wendelmaques can diagnose the voice pipeline and latency requirements, define an evaluation scope, and implement an API, instrumentation, and monitoring dashboard on the client’s infrastructure.
Next step
Send a short description of your voice system and end-of-speech detection challenge to receive a scoped proposal.
Let's assess your project
I reply with scope and a proposal.