ASR Model Adaptation Using LLM-Corrected User Speech Categories
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Solution Overview
Problem
Existing automatic speech recognition (ASR) models face challenges in achieving high accuracy due to labor-intensive ground-truth transcription generation, limited training data diversity, and inefficiencies in utilizing real-world user utterances, particularly in noisy environments, leading to degraded performance.
Innovation Solution
A method to improve already-trained ASR models by accessing user-specific speech-transcription pairs, preprocessing them, and using large language models (LLMs) to correct errors and categorize utterances, focusing training on specific categories with high error rates, thereby personalizing the ASR model to individual users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground-truth transcription is generated by human graders for ASR training, then training data quality is improved, but labor intensity and cost increase significantly
Solution Approach 1:
The system uses the ASR model itself to generate transcriptions for training data, eliminating the need for human graders. The model processes audio files and automatically produces transcriptions that are then used for training, creating a self-service data generation pipeline that significantly reduces labor requirements while maintaining acceptable quality through subsequent correction mechanisms
Solution Approach 2:
The system incorporates feedback loops where initial ASR transcriptions are used to train corrected models, which then generate improved transcriptions. This iterative feedback process allows the system to progressively improve transcription accuracy without requiring manual annotation for each iteration, resolving the contradiction between quality and efficiency
2Adaptability or versatility
If real-world user utterances are used for ASR training, then model adaptability to individual users is improved, but data privacy concerns and data utilization efficiency worsen
Solution Approach 1:
The system segments user data by creating separate training datasets for each user or user group, allowing personalized training without centralized processing of all user data. This segmentation enables selective sharing of data characteristics while maintaining privacy boundaries, resolving the contradiction between adaptability and privacy protection
Solution Approach 2:
The system introduces an intermediary correction model that acts as a mediator between raw ASR outputs and final training data. This intermediary layer processes and corrects transcriptions before they enter the training pipeline, enabling the system to learn from real-world utterances while filtering out privacy-sensitive information and reducing data loss through controlled processing
3Reliability
If ASR models are trained on diverse speech patterns and environments, then model robustness is improved, but training data requirements and system complexity increase
Solution Approach 1:
The system applies partial correction focusing on specific error types and acoustic conditions rather than attempting to correct all possible errors in all data. By prioritizing corrections in high-impact areas such as phonetic transcriptions and noise-resistant segments, the system achieves improved robustness with reduced computational complexity and focused training requirements
Data Source
AI summary
In one embodiment, a method includes accessing a set of speech-transcription pairs for a particular user, each speech-transcription pair including (1) an audio segment spoken by the user and (2) a transcription prediction of the audio segment determined by a trained ASR model. The method further includes generating, by a first LLM, a corrected transcript that corrects one or more errors in at least some of the transcription predictions; classifying, by a second LLM, each of the speech-transcription pairs into one of a number of predetermined speech categories; selecting, based on an error rate, one or more of the predetermined speech categories; and for each selected speech category, further training the trained ASR model based on (1) a subset of audio segments drawn from the respective predetermined speech category and (2) for each audio segment in the subset, the corresponding corrected transcript generated by the first LLM.