Context-Aware Digital Transcription Model for Meeting Accuracy
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Solution Overview
Problem
Conventional systems for conducting and reviewing meetings are inefficient due to inaccurate digital transcriptions, the need for manual review of transcripts, and inflexibility in handling varying vocabulary and meeting contexts.
Innovation Solution
A digital content management system that analyzes audio data, user inputs, and meeting context data to generate accurate and relevant meeting insights, including summaries, highlights, and action items, and automatically updates these insights using machine-learning models trained on past meeting data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional speech recognition systems are used to generate digital transcripts, then transcription can be automated, but accuracy deteriorates due to inability to handle varying vocabulary and meeting contexts
Solution Approach 1:
The system performs preliminary actions by collecting meeting context data (agenda, participants, documents) before transcription occurs. This context is used to pre-train or adjust the speech recognition model, enabling it to anticipate and accurately recognize domain-specific vocabulary and proper nouns that would otherwise be misrecognized.
Solution Approach 2:
The system dynamically changes the parameters of the speech recognition model based on meeting context. By adjusting the model's vocabulary, language model weights, and recognition thresholds according to the specific meeting's domain and participants, the system adapts to handle varying vocabulary and improve accuracy for each unique meeting context.
2Measurement precision
If manual review of transcriptions is performed, then transcription accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs self-service by automatically generating context-aware transcriptions without requiring manual review. The speech recognition model uses meeting context data to self-adjust and produce accurate transcripts autonomously, eliminating the need for time-consuming manual verification while maintaining high accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the speech recognition model continuously learns from meeting context patterns. By analyzing context data and transcription results together, the system refines its recognition accuracy over time, reducing the need for manual intervention while maintaining or improving transcription quality.
3Speed
If conventional transcription systems are used, then processing speed is maintained, but adaptability to different meeting contexts deteriorates
Solution Approach 1:
The system introduces dynamics by making the transcription process adaptive rather than static. The speech recognition model dynamically adjusts its parameters and vocabulary based on meeting context data, allowing it to adapt to different domains, industries, and speaking styles while maintaining processing speed through efficient context analysis and model adjustment.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for improving digital transcripts of a meeting based on user information. For example, a digital transcription system creates a digital transcription model to automatically transcribe audio from a meeting based on documents associated with meeting participants, event details, user features, and other meeting context data. In one or more embodiments, the digital transcription model creates a digital lexicon based on the user information, which the digital transcription system uses to generate the digital transcript. In some embodiments, the digital transcription model trains and utilizes a digital transcription neural network to generate the digital transcript.