genpark-voice-turn-taking-endpoint-detector-skill
OfficialRelated Servers
Alternatives to genpark-voice-turn-taking-endpoint-detector-skill
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables real-time detection of speech turn completion, distinguishing mid-sentence hesitation pauses from finished turns for voice agents and conversational audio pipelines.7MIT
- FlicenseNot gradedqualityBmaintenanceEnables real-time full-duplex voice agents to classify user interruptions, backchannels, and acoustic echo, and to arbitrate turn-taking with context rollback via MCP.-
- FlicenseNot gradedqualityNot gradedmaintenanceEnables real-time voice activity detection, dynamic silence endpointing, and barge-in interruption for conversational voice agents.-
- AlicenseCqualityBmaintenanceEnables real-time voice agents and streaming pipelines to smooth audio playout over volatile network conditions by providing adaptive jitter buffering and packet loss concealment. It exposes an MCP-compliant server that accepts audio frames and telemetry, returning low-latency decision and diagnostic output.4MIT
- FlicenseNot gradedqualityBmaintenanceEnables low-latency conversational turn-taking and barge-in handling for real-time voice AI interactions, orchestrating speech flow with sub-100ms responsiveness.8-
- FlicenseNot gradedqualityNot gradedmaintenanceEnables analysis of acoustic vocal emotion, prosodic valence, and customer frustration from audio inputs through a zero-dependency MCP server.1-
TDQS
Scored across 4 tools
Each tool targets a clearly distinct action: analyze turn status, calibrate thresholds, predict semantic closure, and run benchmarks. There is no overlapping purpose or ambiguous boundary between them.
All names follow a consistent snake_case verb_noun pattern (analyze_, calibrate_, predict_, run_). The convention is predictable and easy to parse.
Four tools is well-scoped for a focused endpoint-detection skill, covering analysis, calibration, prediction, and benchmarking without bloat.
The surface covers core operations: status analysis, threshold calibration, semantic closure prediction, and benchmarking. A minor gap exists for real-time streaming detection or explicit audio input handling, but agents can work around it.