#Speech to text converter manual#
For the evaluation, the authors developed manual summaries of the existing “EDUVSUM” educational videos dataset. The generated summaries by LDA were lengthy thus, a length enhancement method has been proposed. Whereas in the third phase, a summary is generated based on the keywords list. In the second phase, the LDA model is trained on subtitles to generate the keywords list used to extract important sentences. The first phase aims to prepare the subtitle file for modelling by performing some preprocessing steps, such as removing stop words. Specifically, the proposed LDA summarization model follows three phases. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document summarization. Most of the existing studies used Term Frequency–Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures’ summaries. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Videos’ subtitles contain significant information. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. However, such videos and courses are mostly long and thus, summarizing them will be valuable. Nowadays, people use online resources such as educational videos and courses.
Extensive experimentation is performed to validate the efficiency of the proposed method
P class="western" style="margin-top: 0.21cm margin-bottom: 0cm " align="justify"> Speech, is the most powerful way of communication with which human beings express their thoughts and feelings through different languages.
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