  Code-switching (CS) phenomenon occurs when words or phrases from different
languages are alternated in a single sentence. Due to data scarcity, building
an effective CS Automatic Speech Recognition (ASR) system remains challenging.
In this paper, we propose to enhance CS-ASR systems by utilizing rich
unsupervised monolingual speech data within a semi-supervised learning
framework, particularly when access to CS data is limited. To achieve this, we
establish a general paradigm for applying noisy student training (NST) to the
CS-ASR task. Specifically, we introduce the LLM-Filter, which leverages
well-designed prompt templates to activate the correction capability of large
language models (LLMs) for monolingual data selection and pseudo-labels
refinement during NST. Our experiments on the supervised ASRU-CS and
unsupervised AISHELL-2 and LibriSpeech datasets show that our method not only
achieves significant improvements over supervised and semi-supervised learning
baselines for the CS task, but also attains better performance compared with
the fully-supervised oracle upper-bound on the CS English part. Additionally,
we further investigate the influence of accent on AESRC dataset and demonstrate
that our method can get achieve additional benefits when the monolingual data
contains relevant linguistic characteristic.
