Real-Time Meeting Transcription: How MigMig Works Behind the Scenes

The Challenge of Real-Time Transcription

Real-time meeting transcription sounds simple in theory: capture audio, convert speech to text, and display it instantly. In practice, it is one of the most technically challenging problems in software engineering. The audio must be captured without quality loss, processed with minimal latency, and displayed accurately — all while the conversation continues at natural speed.

MigMig solves this problem through a sophisticated pipeline that combines system-level audio capture, AI-powered speech recognition, and intelligent answer generation — all running locally on your machine in real time.

Audio Capture: The Foundation

System-Level Audio Capture

The first step in MigMig's pipeline is capturing the audio from your interview. Unlike applications that require you to speak into a microphone or use virtual audio devices, MigMig captures audio directly from your system's audio output.

This is critical for two reasons:

1. No virtual audio devices — Meeting platforms like Zoom and Teams actively scan for virtual audio devices. If they detect one, they may flag the meeting as suspicious. MigMig avoids this entirely by using system-level audio capture that does not create any virtual devices.

2. Complete audio capture — By capturing system audio, MigMig captures everything your interviewer says through your speakers or headphones. This means every question, follow-up, and comment is transcribed accurately.

Audio Processing Pipeline

Once the raw audio is captured, MigMig processes it through several stages:

Noise Reduction — Background noise from your environment (fans, keyboard clicks, ambient sounds) is filtered out to improve transcription accuracy. The noise reduction algorithm is trained on thousands of hours of real meeting audio.

Audio Chunking — The continuous audio stream is split into small segments (typically 1-2 seconds each) for processing. This allows MigMig to provide transcription results in real time rather than waiting for the entire conversation to finish.

Voice Activity Detection — MigMig distinguishes between speech and silence, ensuring that only relevant audio is sent to the transcription engine. This reduces processing overhead and improves accuracy.

AI Transcription: Converting Speech to Text

The Transcription Engine

MigMig uses state-of-the-art speech-to-text AI models to convert audio into text. These models are trained on millions of hours of spoken content across dozens of accents, speaking styles, and domain-specific terminology.

The transcription engine handles:

• Multiple English accents — American, British, Australian, Indian, and more
• Technical jargon — Programming languages, frameworks, algorithms, and system design terminology
• Rapid speech — Fast-talking interviewers who do not pause between sentences
• Overlapping speech — Moments where both you and your interviewer speak simultaneously
• Background noise — Coffee shop interviews, home office environments, and open-plan workspaces

Latency Optimization

The key to real-time transcription is latency. MigMig targets a transcription delay of less than one second from when the interviewer speaks to when the text appears on your screen. This is achieved through:

• Local processing — All transcription runs on your machine, eliminating network round-trip time
• Streaming architecture — Audio is transcribed as it arrives, not after the conversation pauses
• Optimized models — MigMig uses lightweight AI models that balance accuracy with speed
• Efficient memory management — The transcription buffer is continuously processed to prevent buildup

Answer Generation: From Transcription to Guidance

Context-Aware AI

Once a question is transcribed, MigMig's AI engine analyzes the full context of the conversation to generate a relevant answer. This is not simple keyword matching — the AI understands:

• Conversational context — What has been discussed so far
• Interview type — Whether this is behavioral, technical, or general
• Question intent — Whether the interviewer is asking for a specific example, a technical explanation, or a high-level overview
• Your background — Your resume, skills, and experience (if provided)

Response Generation Modes

Behavioral Mode generates STAR-method answers structured around Situation, Task, Action, and Result. The AI identifies the competency being assessed (leadership, conflict resolution, teamwork, etc.) and structures the answer accordingly.

System Design Mode provides architectural guidance, including high-level system diagrams, database schema suggestions, scalability considerations, and trade-off analysis. The AI adapts to the specific system being designed.

LeetCode Mode suggests algorithmic approaches, provides time and space complexity analysis, and generates clean, interview-ready code in the language of your choice.

General Mode offers concise talking points for culture-fit questions, company research summaries, and role-specific technical explanations.

Stealth Architecture: Why MigMig Cannot Be Detected

The Detection Problem

Meeting platforms invest heavily in detecting unauthorized tools and bots. Zoom, Google Meet, Microsoft Teams, and Webex all employ multiple detection mechanisms to identify:

• Meeting bots and recording tools
• Virtual audio devices
• Screen capture software
• Unauthorized AI assistants

These platforms scan for signatures, process names, audio device registries, and network traffic patterns. Any tool that tries to join a meeting as a participant or create virtual audio devices will eventually be detected.

MigMig's Stealth Approach

MigMig takes a fundamentally different approach. Instead of trying to evade detection through obfuscation, it avoids triggering detection mechanisms entirely by:

No Meeting Participation — MigMig does not join your interview as a participant. It does not appear in the participant list, does not send any data to the meeting server, and does not interact with the meeting platform in any way. It simply listens to your system audio.

No Virtual Devices — MigMig captures audio at the system level without creating any virtual audio devices. Meeting platforms cannot detect what does not exist.

Standard Process Identity — MigMig's process name and signature are indistinguishable from normal system processes. It does not advertise itself, does not create unusual network connections, and does not consume resources in patterns that detection algorithms would flag.

Local-Only Processing — All transcription and AI processing happens on your machine. No data is sent to external servers during the interview, which means there is no suspicious network traffic to analyze.

The 20+ Check System

MigMig continuously validates its undetectability against over 20 different detection checks, including:

1. Audio fingerprinting detection
2. Virtual device enumeration
3. Process name scanning
4. Network traffic analysis
5. CPU/memory usage pattern detection
6. Meeting participant list verification
7. Screen capture detection
8. Input device registration checks
9. Browser extension scanning
10. And many more

Each check is tested against the latest versions of Zoom, Google Meet, Microsoft Teams, and Webex to ensure MigMig remains undetectable.

Performance and Resource Usage

MigMig is designed to run alongside your video conferencing application without impacting performance. On a modern laptop:

• CPU usage: 3-5% during transcription
• Memory usage: 150-250 MB
• Disk usage: Minimal (transcription cache only)
• Network usage: Zero during interviews (all processing is local)

This means you can run MigMig alongside Zoom, your browser, and other applications without noticing any performance degradation.

Privacy and Data Security

MigMig processes all audio locally on your machine. No interview audio is transmitted to external servers. Your transcription data is stored locally and encrypted. You control your data completely.

This local-first architecture means:

• Your interview content never leaves your device
• No cloud storage of sensitive conversation data
• No data sharing with third parties
• Complete compliance with privacy expectations

The Future of Transcription Technology

Real-time transcription technology is advancing rapidly. MigMig's architecture is designed to incorporate new AI models as they become available, improving accuracy and reducing latency over time. The future includes:

• Multi-language support — Transcription in languages beyond English
• Speaker diarization — Automatic identification of who is speaking
• Sentiment analysis — Understanding the emotional tone of the conversation
• Meeting summaries — Automatic generation of post-interview notes

MigMig is at the forefront of these advances, continuously improving its transcription and AI capabilities to provide the best possible interview assistance.

Download MigMig for free to experience real-time AI transcription and assistance during your next interview.