Meeting Speech Biasing and Document Generation
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Solution Overview
Problem
Current video meeting technologies face inefficiencies due to limitations in speech recognition, particularly when discussing varied topics, leading to inaccurate transcription and incomplete action items, as common speech recognition applications struggle with industry-specific terminology and context.
Innovation Solution
Implementing a system that biases automated speech recognition (ASR) using relevant meeting documents and content, generating meeting summaries and action items by incorporating visual and audio cues, and prioritizing content based on participant interactions and document relevance, thereby improving transcription accuracy and streamlining meeting documentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If common speech recognition applications are used to transcribe meeting audio, then the system is simple and easy to operate, but the transcription accuracy deteriorates when industry-specific terminology and context are involved
Solution Approach 1:
The system performs preliminary actions by analyzing meeting documents and extracting terminology before the speech recognition process. This pre-processing creates a customized vocabulary and context model that improves transcription accuracy for industry-specific terms without requiring complex real-time adjustments during speech recognition
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between meeting documents and the speech recognition engine. This intermediary processes document content to generate context-aware suggestions and corrections, enabling accurate transcription of specialized terminology without directly modifying the core speech recognition system
2Productivity
If manual note taking is performed by participants during meetings, then the notes can be referenced later for action items, but participants miss out on certain talking points and the process is time-consuming
Solution Approach 1:
The system enables self-service by automatically generating meeting summaries and action items without requiring manual note-taking from participants. The speech recognition system processes meeting audio, identifies key talking points, and creates structured documentation that captures all discussion points while participants focus on meeting participation
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring meeting audio and comparing it against extracted terminology from meeting documents. This real-time feedback allows the system to correct transcriptions, identify action items, and generate accurate summaries that reflect the actual meeting content, preventing information loss
3Loss of information
If transcripts are generated without context from speech recognition, then the transcription process is fast and automated, but the transcripts do not reflect the importance of certain talking points over others
Solution Approach 1:
The system applies local quality by differentiating the treatment of various talking points based on their importance. It identifies key terms and phrases in meeting documents and uses them to weight and prioritize transcription segments, ensuring that critical discussion points are highlighted while less important content is appropriately summarized or omitted
Solution Approach 2:
The system changes parameters by adjusting the relevance scores of different speech segments based on their association with meeting document terminology. By dynamically modifying these parameters, the system automatically prioritizes important talking points in the transcript without requiring manual review time
Data Source
AI summary
Implementations relate to an application that can bias automatic speech recognition for meetings using data that may be associated with the meeting and/or meeting participants. A transcription of inputs provided during a meeting can additionally and/or alternatively be processed to determine whether the inputs should be incorporated into a meeting document, which can provide a summary for the meeting. In some instances, entries into a meeting document can be designated as action items, and those action items can optionally have conditions for reminding meeting participants about the action items and/or for determining whether an action item has been fulfilled. In this way, various tasks that may typically be manually performed by meeting participants, such as creating a meeting summary, can be automated in a more accurate manner. This can preserve resources that may otherwise be wasted during video conferences, in-person meetings, and/or other gatherings.