VAD

Voice activity detection (VAD) is responsible for determining whether the user is still speaking; modern systems combine semantic understanding with acoustic features to identify turn-taking moments.

传统语音AI普遍采用级联式架构，先用ASR将语音转文字，再交给语言模型处理，最后用TTS合成输出

Achieving an interruptible real-time conversational experience involves a technical pipeline covering voice activity detection (VAD), automatic speech recognition (ASR), intent understanding and context management, retrieval and generation, TTS synthesis, and seamless continuity.

800毫秒是语音代理端到端延迟的业界公认阈值，人类面对面对话回应延迟通常在200-500毫秒之间

Traditional voice activity detection (VAD) methods rely on energy thresholds and silence duration to determine whether speech has ended, which is prone to misjudgment in real conversations.

Enabling VAD (Voice Activity Detection) preprocessing to first remove silent segments before feeding audio into the model can significantly reduce hallucination.

Speaker Diarization is an unsupervised task that requires no pre-registered voiceprints; its technical pipeline includes voice activity detection (VAD), speaker embedding extraction (based on d-vectors or x-vectors), and clustering algorithms.

In telephony scenarios, a VAD that is too sensitive to pauses will interrupt the user, while one that is too sluggish creates dead air; the trailing drawls and speech-rate variations common in Arabic amplify this difficulty.

Turn Detection is the mechanism for judging whether the user has finished speaking, typically combining voice activity detection (VAD) and semantic endpoint detection.

Older users are the most affected by the AI interruption problem, because speech rate, pausing habits and confirming filler words vary greatly across groups, and a fixed threshold is hard to fit to all of them at once.

Semantic completeness judgment adds a layer after VAD: when a suspected turn end is detected, a lightweight language model judges whether the current transcript forms a complete semantic unit, and the AI responds only if the semantics are complete, otherwise the waiting window is extended.

Combining VAD with semantic completeness judgment is more refined than simply lowering interrupt sensitivity, at the cost of 100-300 milliseconds of added latency, requiring a trade-off between "not interrupting" and "responding promptly."

Half-duplex systems use a strict serial pipeline of "voice activity detection (VAD) — recognition — generation — synthesis", so user pauses or inserted filler words may be misjudged as finishing speaking.

End-to-end models internalize turn detection as part of the model's capabilities, making joint decisions based on semantic and acoustic cues, thereby reducing reliance on independent VAD rules.

A typical voice agent pipeline is: VAD determines whether the user has finished speaking, ASR outputs text, the LLM generates a reply, and TTS synthesizes speech and pushes it back.

The root of the bug lay in a defect in a native vertex library: the AI could not accurately determine when the user stopped speaking, and the high-sensitivity microphone easily misjudged ambient noise as voice input.

The response speed of the barge-in path should be included in the latency budget: after the system detects user speech, it must immediately stop playing audio, issue cancellation to the in-progress LLM and TTS, and switch back to the listening and endpoint-detection state.

Streaming processing requires all three stages of STT, LLM, and TTS to work incrementally; VAD is used to determine whether the user has finished speaking, and its sensitivity setting is a trade-off between latency and false interruptions; the interruption mechanism requires that when the user interjects mid-way, the system immediately stops the current TTS playback and clears the subsequent inference queue.

The typical pipeline for speaker diarization includes four steps: voice activity detection (VAD), speech segmentation, speaker embedding extraction and clustering.

VoxRT's voice activity detector (VAD) runs at 3% CPU on mid-range Android (Snapdragon 662 level).