Context-Aware Speech-to-Text Transcription Using Nested Language Models
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Solution Overview
Problem
Existing conferencing systems generate text transcripts that often lack context and fail to accommodate varying user expertise, leading to inconsistent results due to the lack of organization-specific and user-specific terminology handling.
Innovation Solution
A multi-layered approach using convolutional neural networks with organization-specific, user-specific, and agenda-specific language models to generate accurate text transcripts, incorporating metadata from calendar events and user interactions to tailor transcripts to individual user preferences and domain knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general speech-to-text feature is used in conferencing services, then text transcripts can be generated for hearing impaired individuals and later review, but the transcripts lack context and domain-specific accuracy
Solution Approach 1:
The patent introduces an intermediary processing layer between the audio input and text output that incorporates organization-specific language models, user-specific preferences, and context retrieval mechanisms. This intermediary layer enriches the basic transcription by inserting domain-specific terminology, expanding acronyms based on organizational context, and personalizing the output according to user expertise levels.
Solution Approach 2:
The transcription system is segmented into multiple specialized components: a general speech-to-text engine, an organization-specific language model layer, a user preference layer, and a context retrieval system. Each segment handles a specific aspect of the transcription process, allowing the system to maintain base transcription functionality while adding layers of contextual accuracy and personalization.
2Productivity
If existing systems generate text transcripts from soundtrack, then transcripts are produced for user review, but results are inconsistent depending on user expertise level
Solution Approach 1:
The system dynamically adapts the transcript generation process based on user expertise levels and organizational context. Language models are selectively applied based on the detected domain and user profile, allowing the system to adjust between providing detailed expansions for novice users and more concise outputs for expert users, thereby maintaining both efficiency and adaptability.
Solution Approach 2:
The patent changes key parameters of the transcription process based on user attributes, including the expansion level of acronyms, the detail of domain-specific terminology, and the selection of organization-specific language models. These parameter changes allow the same base transcription to be customized for different user expertise levels while maintaining consistent generation efficiency.
3Measurement precision
If organization-specific and user-specific language models are integrated, then highly accurate and personalized transcripts are generated, but system complexity increases
Solution Approach 1:
The patent implements a nested architecture where organization-specific language models contain user-specific preferences, which in turn contain agenda-specific customizations. This nested structure allows multiple layers of specificity to be integrated without creating a flat, unmanageably complex system. Each nested layer builds upon the previous one, adding specificity while maintaining a hierarchical organization that simplifies management.
Solution Approach 2:
The system employs universal language model architectures that can serve multiple functions across different organizations and users. Rather than creating entirely separate models for each organization and user, the patent uses a multi-functional model framework that can be configured and customized through parameters and training data, reducing overall system complexity while maintaining high accuracy.
Data Source
AI summary
Disclosed are various approaches for generating a text transcript of a soundtrack. The soundtrack can correspond to an event in a conferencing service. Language models can be trained on data that is specific to organizations, users within the organization, and metadata associated with an agenda for the event. The metadata can include texts, attachments, and other data associated with the event. The language models can be arranged into a convolutional neural network and output a text transcript. The text transcript can be used to retrain the language models for subsequent use.