How Real-Time Interview Speech-to-Text Works

Real-time interview transcription depends on capturing two separate audio streams at once:

• System audio (loopback) — the interviewer's voice arriving through Zoom, Google Meet, or Microsoft Teams.
• Microphone audio — your own voice as you speak.

SubcueAI's native desktop app captures both streams simultaneously using standard operating-system audio APIs available on macOS and Windows. Because the capture happens at the OS level — not inside the meeting app itself — no browser plugin or meeting bot is required. The combined stream is then passed to the speech recognition engine.

Once audio is captured, it moves through a streaming speech-to-text pipeline that works in short, overlapping audio chunks rather than waiting for a complete sentence. This approach keeps latency low — typically a matter of seconds from speech to readable text.

• Voice Activity Detection (VAD) filters silence so the engine only processes frames that contain speech, reducing noise and saving processing time.
• Acoustic modeling maps audio features to phonemes, then to words, using a neural network trained on large speech datasets.
• Language modeling ranks word sequences by probability, improving accuracy for technical vocabulary and proper nouns common in interviews.

The result is a rolling transcript that updates continuously as the conversation progresses.

The live transcript is the input to SubcueAI's answer-suggestion layer. When the system detects that a question has been asked — based on sentence structure and punctuation cues — it sends the relevant context to a large language model (LLM) that generates a suggested response.

• Suggestions appear in SubcueAI's floating local overlay, visible only on your screen — not shared to the meeting window.
• The overlay is designed to stay out of any shared-screen region so it is not visible to participants watching your screen share.
• You can read, adapt, or ignore any suggestion; the tool is meant to support your thinking, not script it word-for-word.

See the setup tutorial for guidance on positioning the overlay before your interview.

Real-time transcription quality depends on several factors outside any app's full control:

• Microphone quality and background noise — a headset microphone significantly improves accuracy over a built-in laptop mic.
• Internet connection — if the AI inference step is cloud-assisted, network latency adds to response time.
• Accents and speaking pace — modern neural speech models handle a wide range of accents but are not perfect.
• Proctored or recorded interviews — SubcueAI's overlay is local and private, but in screen-recorded or proctored environments the overlay could appear in a recording if not carefully positioned or hidden. Always review the rules of your specific interview before using any assistance tool.

For a broader look at privacy and what interviewers can see, visit the security and privacy page.