Why WebAudio Beats the Speech API for Detecting Silence in Voice Apps

A developer building a browser-based AI mock interview tool found that relying on the Web Speech API to detect when a user stops talking produces unreliable results due to network latency, audio buffering, and the recognizer rewriting interim transcripts. These issues cause the system to misread active speech as silence and genuine pauses as ongoing activity. To fix this, the developer instead uses a small WebAudio graph that reads raw microphone waveform data and calculates loudness via root-mean-square analysis, making silence detection a direct acoustic measurement. Two separate thresholds distinguish a brief thinking pause from a completed answer, preventing the AI interviewer from either cutting the user off or waiting indefinitely. The Speech API is kept solely for transcription, while all timing and turn-taking logic is handled by the waveform signal.