kNN-CTC: Enhancing ASR via Retrieval of CTC Pseudo Labels

Abstract

The success of retrieval-augmented language models in various natural language processing (NLP) tasks has been constrained in automatic speech recognition (ASR) applications due to challenges in constructing fine-grained audio-text datastores. This paper presents kNN-CTC, a novel approach that overcomes these challenges by leveraging Connectionist Temporal Classification (CTC) pseudo labels to establish frame-level audio-text key-value pairs, circumventing the need for precise ground truth alignments. We further introduce a skip-blank strategy, which strategically ignores CTC blank frames, to reduce datastore size. kNN-CTC incorporates a k-nearest neighbors retrieval mechanism into pre-trained CTC ASR systems, achieving significant improvements in performance. By incorporating a k-nearest neighbors retrieval mechanism into pre-trained CTC ASR systems and leveraging a fine-grained, pruned datastore, kNN-CTC consistently achieves substantial improvements in performance under various experimental settings. Our code is available at https://github.com/NKU-HLT/KNN-CTC.