Real-Time Audio Word Clouds for Speaker Distinction
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Solution Overview
Problem
Current methods for summarizing audio content, such as word clouds, are limited in their ability to distinguish between different subjects and speakers within a conversation, and are typically designed for text-based formats, making them cumbersome for auditory conversations or meetings.
Innovation Solution
A computer-implemented method and system that uses speech and speaker recognition software to convert audio content into text, generate word clouds for each segment of a meeting, and visually represent key words and speakers, allowing for real-time visualization of meeting content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a single word cloud is generated for the entire audio content, then the summary provides an overall view of the conversation, but it cannot distinguish between different speakers or subjects, reducing the clarity and utility of the summary
Solution Approach 1:
The patent divides the audio content into multiple segments based on speaker identification. Each segment generates a separate word cloud that represents only that speaker's contributions. This segmentation preserves speaker distinction information while maintaining manageable complexity through automated speaker detection and separate processing of each speaker's transcript.
2Loss of information
If speech recognition software is used to convert audio to text, then the audio content becomes analyzable, but the process is time-consuming and reduces real-time capability
Solution Approach 1:
The system performs preliminary speaker identification and segmentation of audio content before generating word clouds. By pre-organizing the audio into speaker-specific segments, the system reduces the computational burden during the word cloud generation phase, enabling faster processing and improved real-time performance.
3Loss of information
If comprehensive speech recognition is applied to capture all spoken content, then complete transcription is achieved, but the resulting transcript becomes complex and difficult to review
Solution Approach 1:
The patent extracts and displays only the most significant words from each speaker's transcript in visual word cloud format. This extraction process filters out less important words while preserving key information, making the transcript reviewable through visual prominence of important terms rather than reading through complete text.
Solution Approach 2:
The system uses color coding to differentiate between speakers in the word cloud visualization. Each speaker's words are displayed in distinct colors, allowing reviewers to quickly identify which speaker said what without needing to read through complete transcripts, thereby improving reviewability while maintaining completeness.
Data Source
AI summary
Audio content is converted to text using speech recognition software. The text is then associated with a distinct voice or a generic placeholder label if no distinction can be made. From the text and voice information, a word cloud is generated based on key words and key speakers. A visualization of the cloud displays as it is being created. Words grow in size in relation to their dominance. When it is determined that the predominant words or speakers have changed, the word cloud is complete. That word cloud continues to be displayed statically and a new word cloud display begins based upon a new set of predominant words or a new predominant speaker or set of speakers. This process may continue until the meeting is concluded. At the end of the meeting, the completed visualization may be saved to a storage device, sent to selected individuals, removed, or any combination of the preceding.