用轻量方法让语音助手只听指定用户说话，减少误触发。
HyWA: Architecture-Preserving Personalized Voice Activity Detection for Full-Duplex Voice Assistants
- 通过超网络生成说话人相关权重，不改原有模型结构
- 实测将误打断率从88.9%降至9.9%
- 适合需要低延迟部署的智能设备语音系统
语音活动检测（VAD）是智能设备语音助手流水线中的早期入口。传统VAD对任何说话人响应，导致邻近对话和助手下播残留引发误触发，降低体验并浪费算力。个性化语音活动检测（PVAD）通过仅识别注册目标说话人来解决此问题。现有方法通常在输入或隐藏层融入说话人信息，需修改VAD架构，增加部署与认证成本。本文提出HyWA，一种基于超网络的权重适配方法，可在不改变声学接口与推理拓扑的前提下，将通用VAD转换为PVAD。HyWA在注册时生成一次说话人相关权重，无需每用户优化。我们将HyWA应用于预训练的阿里FSMN与NVIDIA MarbleNet VAD模型。在合成与真实用户测试数据上，均实现目标说话人区分度提升与误触发抑制。在全双工抢断流程中，用HyWA-PVAD替代通用VAD后，误打断检测率由88.9%降至9.9%。
原文摘要 · Abstract (English)
Voice activity detection (VAD) serves as an early gate in voice-assistant pipelines for smart devices. Because conventional VADs respond to speech from any speaker, nearby conversations and residual assistant playback lead to unwanted triggers, degrade the user experience, and waste computational resources. Personalized voice activity detection (PVAD) addresses this limitation by detecting speech only from an enrolled target speaker. Existing PVAD methods typically incorporate speaker information into model inputs or hidden representations. These approaches require VAD architectural changes that increase engineering and requalification costs in deployment. We present HyWA, a hypernetwork-based weight-adaptation method that converts an established VAD into a PVAD, while preserving its acoustic interface and inference topology. HyWA generates speaker-conditioned weights once at enrollment and requires no per-user optimization. We apply HyWA to pretrained Alibaba's FSMN and NVIDIA's MarbleNet VAD models. Evaluations on synthetic and real-user test data show consistent improvements in target-speaker discrimination and false-positive suppression. In an integrated full-duplex barge-in pipeline, replacing generic VAD with HyWA-based PVAD reduces observed false-interruption detections from 88.9% to only 9.9%.
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