ASR Domain Adaptation Using RAG for Low-Confidence Terms
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face challenges in accurately transcribing speech from specific domains due to limited training data and the need for costly and time-consuming retraining, especially when dealing with niche knowledge domains like medical or technical fields, where specialized vocabulary and terminology are prevalent.
Innovation Solution
A retrieval augmented generation (RAG) pipeline is used to improve ASR accuracy by leveraging domain-specific knowledge bases to correct low-confidence words, allowing for adaptability across various domains without retraining the ASR model. This involves using a domain-adapted retriever to retrieve relevant terminology and context from unstructured data sources like PDFs, webpages, and images, which are then processed by a large language model to generate accurate transcripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the ASR model is retrained for each niche domain, then the transcription accuracy for that domain is improved, but the time and cost increase significantly
Solution Approach 1:
The system segments the domain adaptation process into two independent parts: a general ASR model that remains unchanged and a domain-specific knowledge base that is dynamically retrieved. This allows the system to adapt to niche domains without retraining the entire ASR model, thus maintaining high transcription accuracy while reducing retraining time and cost.
Solution Approach 2:
The patent introduces a retrieval augmented generation (RAG) pipeline as an intermediary between the ASR model and domain-specific knowledge. The RAG system retrieves relevant domain terminology and context from knowledge bases and uses it to correct low-confidence words in transcriptions, enabling domain adaptation without model retraining.
2Measurement precision
If the ASR model is retrained for each niche domain, then the transcription accuracy for that domain is improved, but the model size and complexity increase
Solution Approach 1:
The system separates the ASR model from domain-specific knowledge, storing domain terminology in external knowledge bases rather than embedding it in the model. This segmentation allows the ASR model to remain small and general-purpose while domain-specific information is accessed on-demand through retrieval operations.
Solution Approach 2:
The RAG pipeline acts as an intermediary that bridges the general ASR model with domain-specific knowledge bases. It retrieves relevant domain terminology and uses it to correct transcriptions, enabling the system to handle niche domains without increasing the ASR model size or complexity.
3Measurement precision
If manual training data preparation is performed for each domain, then the ASR model performance is improved, but the time and resource consumption increase
Solution Approach 1:
The system employs automated retrieval operations to access domain-specific knowledge from pre-existing knowledge bases, eliminating the need for manual data preparation. The RAG pipeline automatically retrieves relevant terminology and context, and the system self-corrects low-confidence words using this retrieved knowledge, significantly improving productivity.
Solution Approach 2:
Domain-specific knowledge bases are pre-built and indexed before actual transcription tasks. This preliminary preparation allows the retrieval system to quickly access domain terminology when needed, eliminating the time-consuming manual data preparation process during actual ASR operations.
4Measurement precision
If domain-specific training data is collected and processed, then the ASR accuracy for that domain is improved, but data privacy risks increase
Solution Approach 1:
The RAG pipeline serves as an intermediary layer that retrieves domain-specific knowledge from secure knowledge bases without requiring raw data to be stored or processed in the ASR model. This architecture maintains data privacy by keeping sensitive information isolated in separate knowledge bases while still enabling accurate domain-specific transcription through retrieved context.
Data Source
AI summary
Approaches presented herein provide for the generation of text transcripts of speech represented in audio data. In particular, an automatic speech recognition (ASR) model can be used together with a retrieval augmented generation (RAG) pipeline to provide for improvement of transcripts that include terminology related, or specific, to a specific knowledge domain. A knowledge base for a given domain can include a number of files or documents in a number of different formats (e.g., documents, images, and webpages) that do not need to be cleaned, classified, or curated. When an ASR generates a transcript where at least one word has a confidence level that falls below a confidence threshold, that transcript can be passed to a language model of the RAG pipeline which can use the retrieved domain-specific data to attempt to identify the appropriate words or terms to use to replace the words tagged as having low confidence.