Contextual Speech Recognition for Virtual Meeting Jargon
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Solution Overview
Problem
Virtual meeting systems often struggle with accurately transcribing company-specific or team-specific jargon due to systemic failures in speech recognition, leading to less useful or confusing generated text.
Innovation Solution
A speech recognition system is modified by identifying and incorporating previously unrecognized content, such as names, documents, and chat messages, using optical character recognition and machine learning models to improve transcription accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a speech recognition system uses general training data, then it can process diverse speech patterns, but it fails to accurately recognize company-specific or team-specific jargon
Solution Approach 1:
The system performs preliminary actions by extracting text from meeting content items (documents, slides, chat messages) before the actual speech recognition occurs. This extracted text is then used to modify the speech recognition model in advance, enabling it to recognize specialized jargon accurately during the meeting transcription process
Solution Approach 2:
The system uses feedback from the meeting context by extracting text from content items and using this feedback to modify the speech recognition model. The extracted text serves as feedback information that updates the model's understanding of company-specific terminology, improving subsequent recognition accuracy
2Measurement precision
If the speech recognition system is modified using extracted text from content items, then transcription accuracy improves, but system complexity increases
Solution Approach 1:
The system merges multiple functions into a unified process: content item extraction, text processing, and speech recognition modification are combined into an integrated system that operates together to improve transcription accuracy without requiring entirely separate complex subsystems
Solution Approach 2:
The extracted text from content items serves as an intermediary element that bridges the gap between the raw audio data and the speech recognition model. This intermediary text representation enables the model to understand and accurately transcribe specialized jargon without fundamentally redesigning the entire system architecture
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of text generation from speech audio, reducing computational resources wasted on inaccurate representations and improving the usability of virtual meeting transcripts and summaries.
Implementation Method 1
processing the image using optical character recognition
Implementation Method 2
machine learning models trained to convert speech data to corresponding text data
Data Source
AI summary
A method includes receiving audio data of a virtual meeting and identifying, within a plurality of content items related to the virtual meeting, content not previously recognized by a speech recognition system designated to convert the audio data of the virtual meeting into text. The method also includes causing the speech recognition system to be modified based on the previously unrecognized content. The method further includes causing the audio data of the virtual meeting to be converted into the text using the modified speech recognition system, wherein the text comprises at least part of the previously unrecognized content.