A Comparative Study for Speech Summarization Based on Machine Learning: A Survey | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 11, Volume 16, Issue 2, December 2022, Pages 89-96 PDF (768.93 K) | ||
| Document Type: Review Paper | ||
| DOI: 10.33899/csmj.2022.176595 | ||
| Authors | ||
| Hiba Adreese Altememi* 1; Yusra Faisal Al-Irhaim2 | ||
| 1علوم الحاسوب | ||
| 2College of Computer Sciences and Mathematics University of Mosul | ||
| 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. | ||
| Keywords | ||
| ASR; LSTM; speech summarization; Deep learning; ROUGE | ||
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