LLM-Guided Audio Diffusion for Noisy ASR Transcription
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Solution Overview
Problem
Automatic speech recognition (ASR) systems face challenges in accurately transcribing spoken language due to background noise, speaker accents, variations in recording quality, and out-of-domain terms, leading to errors in the final transcription.
Innovation Solution
A method that involves encoding original input audio features into audio encodings using a speech recognition model, processing these encodings through a sequence processing neural network to generate text embeddings, and determining an audio correction parameter with a diffusion model to modify the input audio features, improving the accuracy of the final transcription.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard ASR model is used for speech recognition, then the system is simple and fast, but accuracy deteriorates in the presence of background noise, accents, and out-of-domain terms
Solution Approach 1:
The system segments the speech recognition task into multiple processing stages: initial ASR transcription, LLM-based error identification, diffusion model-based audio correction, and final ASR transcription. This segmentation allows each component to specialize in specific aspects, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by first generating an initial transcription and identifying potential errors using the LLM before applying audio correction. The diffusion model receives guidance from identified error patterns and contextual information in advance, allowing it to pre-adjust audio features to correct anticipated transcription errors before the final recognition stage.
2Measurement precision
If audio correction is applied to all input audio features, then transcription accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system applies audio correction selectively rather than uniformly to all audio features. The diffusion model is guided by error patterns identified in the initial transcription, allowing it to focus computational resources on correcting specific problematic segments or types of errors (such as background noise interference, accent-related misrecognitions, or out-of-domain term errors) while leaving other segments unchanged.
3Measurement precision
If the diffusion model is conditioned on multiple inputs (audio encodings, text embeddings, initial transcription), then correction accuracy improves, but model complexity and computational load increase
Solution Approach 1:
The diffusion model is designed with multi-functionality to process and integrate multiple types of conditional inputs simultaneously: audio encodings from the ASR model, text embeddings from the LLM, and error patterns from the initial transcription. This universal conditioning mechanism allows the model to adaptively adjust audio features based on the combined information from all sources, improving correction accuracy while maintaining a unified model architecture.
Data Source
AI summary
A method for improving automatic speech recognition (ASR) includes receiving a sequence of original input audio features characterizing a spoken utterance and encoding, using an audio encoder of a speech recognition model, the original input audio features into a sequence of original audio encodings. A sequence processing neural network, such as a large language model, processes the original audio encodings to generate a sequence of text embeddings. A diffusion model, conditioned on the text embeddings, determines an audio correction parameter. The method also includes modifying the original input audio features based on the audio correction parameter to generate a sequence of modified input audio features. The speech recognition model then processes the modified input audio features to generate a final transcription of the spoken utterance.