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- W4313245852 abstract "The most important aspect of human communication is speech. Lengthy media such as speech takes a long time to read and understand. This difficulty is solved by providing a reduced summary with semantics. Speech summarization can either convert speech to text using automated speech recognition (ASR) and then build the summary, or it can process the speech signal directly and generate the summary. This survey will look at a various of recent studies that have used machine and deep learning algorithms to summarize speech. it discusses the speech summarizing literatures in terms of time restrictions, research methodology, and lack of interest in particular databases for literature searches. As newer deep learning approaches were not included in earlier surveys, this is a new survey in this discipline where different approaches with various datasets were explored for speech summarization and evaluated using subjective or objective methods." @default.
- W4313245852 created "2023-01-06" @default.
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- W4313245852 date "2022-12-27" @default.
- W4313245852 modified "2023-10-14" @default.
- W4313245852 title "A Comparative Study for Speech Summarization Based on Machine Learning: A Survey" @default.
- W4313245852 doi "https://doi.org/10.33899/csmj.2022.176595" @default.
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