PODCAST TRANSCRIPTION AND SUMMARIZATION WITH SPEECH SYNTHESIS

Abstract

We propose a system which automatically generate audio summaries for podcasts, allowing listeners to quickly preview podcast episodes. The proposed system first transcribes the audio from a podcast using automatic speech recognition (ASR), then summarizes the transcript using extractive text summarization, and finally returns the audio associated with the text summary. Motivated by a lack of relevant datasets for this task, we created our own by transcribing the audio from various podcasts and generating summaries for these transcripts using a manual annotation tool. After transcription, the text is processed through natural language processing (NLP) algorithms to extract the most important points, key topics, and relevant information from the podcast. The first component of the system involves automatic transcription, where speech-to-text algorithms convert audio podcast episodes into textual format. This transcription process employs state-of-the-art machine learning models trained on large datasets to achieve high accuracy and reliability. The system employs natural language processing (NLP) techniques to generate concise summaries of podcast episodes. These summaries aim to capture the essence of the content, providing users with a quick overview of key topics, insights, and discussions covered in the episode. Key Words: Podcasts, audio summarization, automatic speech recognition, automatic summarization.