Filler word removal is a process in cybersecurity and speech processing that involves automatically detecting and deleting non-essential words or sounds, such as 'um', 'ah', or 'like', from speech transcripts or audio recordings. These filler words are commonly used in spontaneous speech but do not contribute meaningful information. Removing them can enhance the readability and clarity of transcripts, improve the efficiency of voice recognition systems, and reduce storage requirements. In cybersecurity, filler word removal can help streamline the analysis of voice communications and minimize irrelevant data, making it easier to identify important information or potential threats. Advanced algorithms and machine learning models are often used to accurately identify and eliminate these fillers while preserving the original meaning and context of the conversation.