Podcast Summarization via Audio Segmentation and Transcript Parsing
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Solution Overview
Problem
Existing content distribution systems struggle to provide effective summaries of podcast content, especially for spoken word podcasts, as they lack the ability to understand the context and meaning of the spoken content.
Innovation Solution
A podcast summarization system that receives podcast content, generates a transcript, parses it to identify audio segments, determine classifications, and extract sentence offsets, then uses this information to divide the transcript into text sentences and select a group of sentences to generate an audio summary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing content distribution systems use traditional summarization methods, then they can provide summaries of podcast content, but they fail to understand the context and meaning of the spoken content
Solution Approach 1:
The patent introduces an automatic speech recognition (ASR) system as an intermediary component that converts spoken podcast content into text transcripts. This intermediary step enables the summarization system to process and understand the content through text-based analysis, thereby recovering contextual information that was lost in traditional audio-only approaches. The ASR transcript serves as a bridge between the audio input and the summarization engine, allowing for meaningful context understanding.
2Measurement precision
If the system generates detailed transcripts and performs comprehensive parsing, then it can accurately identify audio segments and sentence offsets, but the processing time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the podcast content into discrete audio segments and corresponding text sentences. The system identifies specific temporal boundaries (offsets) where audio segments begin and end, and where sentences occur in the transcript. This segmentation approach allows the system to process content in manageable units, improving identification accuracy while enabling efficient processing through localized analysis rather than holistic processing of the entire podcast.
Solution Approach 2:
The system performs preliminary action by generating the complete transcript and identifying all audio segments and sentence offsets before the actual summarization process. This pre-processing step creates a structured framework of temporal markers and segment boundaries that can be quickly referenced during summary generation, reducing the computational burden and processing time during the actual summarization task.
3Productivity
If the system selects and combines multiple audio segments to create a summary, then it can provide a comprehensive representation of the podcast, but the complexity of audio file manipulation increases
Solution Approach 1:
The patent employs copying by creating a text-based representation (transcript) of the audio content that can be manipulated independently. Instead of directly manipulating audio files for analysis and summary creation, the system works with copied text data, identifies relevant segments through text processing, and only performs audio manipulation for the final assembly of selected segments. This copying approach simplifies the overall process by decoupling the analysis phase from the audio manipulation phase.
Data Source
AI summary
In one aspect, a method includes receiving podcast content, generating a transcript of at least a portion of the podcast content, and parsing the podcast content to (i) identify audio segments within the podcast content, (ii) determine classifications for the audio segments, (iii) identify audio segment offsets, and (iv) identify sentence offsets. The method also includes based on the audio segments, the classifications, the audio segment offsets, and the sentence offsets, dividing the generated transcript into text sentences and, from among the text sentences of the divided transcript, selecting a group of text sentences for use in generating an audio summary of the podcast content. The method also includes based on timestamps at which the group of text sentences begin in the podcast content, combining portions of audio in the podcast content that correspond to the group of text sentences to generate an audio file representing the audio summary.