How to Train AI to Detect Silence & Pauses in Speech Files

In human speech, meaning doesn't only live in words. It breathes in the pauses between them. Those silences carry hesitation, suspense, and thought.

Traditional Text-to-Speech systems often sound “mechanical” not because their voices are bad — but because they never learned how to breathe. At VMEG, we approached this problem differently.

🔍 How it works?

• Using ASR (Automatic Speech Recognition), we detect each word's precise timestamp.

• We analyze the time gaps between words to locate real breathing points.

• We adapt pause sensitivity across languages — subtle for Chinese and Japanese, wider for Thai or Burmese.

• These pauses are then encoded as precise tags within TTS, creating speech that feels alive, not robotic.

🛡️ Built-in safeguards

• Skip pauses when ASR confidence is low.

• Filter false silences and clean outputs automatically.

• Standardize rhythm precision to 10ms for stable, natural pacing.

🎧 The result?

Machines that speak with rhythm, not rush. Voices that pause to let thoughts land — just like humans do.