  With the rise of video production and social media, speech editing has become
crucial for creators to address issues like mispronunciations, missing words,
or stuttering in audio recordings. This paper explores text-based speech
editing methods that modify audio via text transcripts without manual waveform
editing. These approaches ensure edited audio is indistinguishable from the
original by altering the mel-spectrogram. Recent advancements, such as
context-aware prosody correction and advanced attention mechanisms, have
improved speech editing quality. This paper reviews state-of-the-art methods,
compares key metrics, and examines widely used datasets. The aim is to
highlight ongoing issues and inspire further research and innovation in speech
editing.
