ASR Adapter Alignment With LoRA to Reduce Insertion Errors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for large language model (LLM)-based automatic speech recognition (ASR) systems face issues such as inefficient module tuning, lack of alignment between adapter and LLM embeddings, and high insertion errors due to limited training data or mismatched test sets, leading to suboptimal performance.
Innovation Solution
A machine learning model architecture that includes a speech encoder, adapter, and decoder, with explicit alignment using a matching loss and cross-attention, fine-tuning with low-rank adaptation (LoRa), and implementing constraints like n-gram non-repetition and length penalties during training and inference to enhance parameter efficiency and reduce insertion errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing LLM-based ASR techniques are used, then speech recognition can be performed, but insertion errors increase and performance deteriorates when training data is limited or test sets are mismatched
Solution Approach 1:
The patent applies preliminary action by pre-training the speech encoder and decoder on large-scale datasets before fine-tuning on task-specific data. This pre-training establishes robust acoustic and text representations that improve reliability when facing limited training data or distribution shifts between training and test sets.
Solution Approach 2:
The patent employs parameter changes through low-rank adaptation (LoRA), which modifies a small subset of model parameters while keeping the majority frozen. This approach enables the model to adapt to different domains and conditions with limited training data, resolving the contradiction between reliability and adaptability by efficiently changing parameters only where necessary.
2Adaptability or versatility
If adapter modules are added to connect speech encoder and decoder, then model functionality is enhanced, but alignment between adapter and LLM embeddings becomes misaligned
Solution Approach 1:
The patent implements feedback by introducing a loss function that explicitly measures and optimizes the alignment between adapter embeddings and LLM text embeddings. During training, this alignment loss provides continuous feedback to adjust adapter parameters, ensuring that the adapter produces embeddings compatible with the LLM's expected input space, thus resolving the misalignment issue while preserving functionality.
Solution Approach 2:
The adapter itself serves as an intermediary component that bridges the speech encoder and the LLM decoder. By designing the adapter with explicit alignment constraints and using it as a mediating layer, the patent enables functional enhancement while maintaining embedding compatibility through the intermediary's optimized transformation.
3Ease of manufacture
If module tuning is performed without explicit alignment constraints, then training is simpler, but representation alignment between acoustic and text embeddings deteriorates
Solution Approach 1:
The patent maintains training simplicity while improving alignment by introducing an additional alignment loss term to the existing training objective. This feedback mechanism guides the adapter learning process to produce better-aligned embeddings without fundamentally changing the training procedure or requiring complex re-engineering of the training pipeline.
Solution Approach 2:
The patent applies partial action by only optimizing certain parameters (adapter weights) while keeping the majority of the model (speech encoder and LLM decoder) frozen. This selective tuning approach maintains ease of training by limiting the number of trainable parameters, while the explicit alignment loss ensures that the adapted representations are properly aligned with the LLM's embedding space.
Data Source
AI summary
The present disclosure describes techniques for improving speech recognition using a machine learning model. The machine learning model comprises a speech encoder configured to generate acoustic representations based on input speech, an adapter configured to generate adapted representations based on the acoustic representations, and a decoder configured to generate text corresponding to the input speech. A matching loss is applied during training the machine learning model. The matching loss is configured to explicitly force acoustic representations generated by the adapter to align with text embeddings. The machine learning model is fine-tuned by employing parameter-efficient low-rank adaptation. The machine learning model is trained to perform automatic speech recognition with performance improvement and parameter efficiency.