People collect voice messages in chat threads every day. Some of those recordings contain information worth keeping in text form. Converting them requires importing the audio, running it through speech recognition, and getting clean output. The process involves several technical layers that determine what comes out the other end.
What Happens When Audio Gets Imported
Voice messages in chat applications are stored in compressed formats. AMR, SILK, and OGG are common. These codecs sacrifice audio fidelity for smaller file sizes, which makes sense when pushing data over a mobile network. Meetingminutes APP processes these formats directly. The import step extracts the raw audio stream, decompresses it into PCM, and feeds it into the transcription pipeline. No third-party conversion tool is needed. The app accepts nine mainstream audio formats and thirteen video containers.
How the Recognition Engine Processes Audio
Under the hood, transcription runs on a neural acoustic model paired with a language model decoder. The acoustic model breaks audio into 25-millisecond frames and converts each frame into phonetic probability distributions. The language model picks the most probable word sequences from those distributions. For clean Mandarin audio recorded in a quiet room, accuracy sits around 98 percent. Real conditions are messier.
Noise Reduction: The Actual Mechanism
Noise reduction happens in stages. Spectral subtraction handles steady-state noise. It analyzes the frequency spectrum, estimates the noise profile from silent intervals, and subtracts that profile from active speech. Fan hum, air conditioning, engine drones all fall in this category.
For non-steady noise — sudden door slams, distant conversations, keyboard clatter — deep neural network-based enhancement takes over. A noise estimation network runs alongside the speech enhancement network, tracking background changes in real time and adjusting enhancement parameters on the fly.
In an office environment with ambient noise around 45 to 55 decibels, the combined approach reduces effective noise by roughly 15 to 20 decibels. That makes a meaningful difference for intelligibility.
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