Beyond Transcription: Why Automating Filler Removal Is More Complex Than It Seems

Removing filler words from audio requires far more than speech-to-text transcription—it demands sophisticated multi-stage audio analysis to handle edge cases that traditional speech recognition misses. Understanding why Whisper alone falls short and how combining detection strategies (word-level analysis, gap detection, and audio waveform inspection) creates reliable automation matters for voice content creators and developers building speech-first applications.