Endpoint Detection
A technology used in voice interaction systems to determine when a user has finished speaking. Traditional approaches rely on acoustic energy thresholds, while newer approaches incorporate semantic completeness analysis to make more accurate turn-end judgments.
Timeline (last 90 days)
Streaming STT relies on endpoint detection to determine when a sentence ends, typically based on silence duration (VAD) and language model confidence.
When the system mistakenly identifies silence as the end of a sentence, it submits results prematurely (premature finalization), causing the semantic content to be truncated.
For multilingual real-time systems, endpoint parameters should be configured differently per language rather than uniformly applied globally.
Modern systems typically layer language model confidence scoring on top of VAD, triggering endpoint submission only when the acoustic signal is silent and the language model determines a complete sentence can be formed.
端侧TTS需在用户说完后200-500ms内开始语音反馈才能保持对话流畅感，要求模型以流式方式逐帧生成音频
All Facts (6)
Streaming STT relies on endpoint detection to determine when a sentence ends, typically based on silence duration (VAD) and language model confidence.
50%UnverifiedWhen the system mistakenly identifies silence as the end of a sentence, it submits results prematurely (premature finalization), causing the semantic content to be truncated.
50%UnverifiedFor multilingual real-time systems, endpoint parameters should be configured differently per language rather than uniformly applied globally.
50%UnverifiedModern systems typically layer language model confidence scoring on top of VAD, triggering endpoint submission only when the acoustic signal is silent and the language model determines a complete sentence can be formed.
50%Unverified端侧TTS需在用户说完后200-500ms内开始语音反馈才能保持对话流畅感，要求模型以流式方式逐帧生成音频
50%Unverified语音端点检测（VAD）负责判断用户何时停止说话，过于保守的VAD设置可能在用户停顿后等待400-800ms才判定句子结束
50%