  Code-switching (CS) speech refers to the phenomenon of mixing two or more
languages within the same sentence. Despite the recent advances in automatic
speech recognition (ASR), CS-ASR is still a challenging task ought to the
grammatical structure complexity of the phenomenon and the data scarcity of
specific training corpus. In this work, we propose to leverage large language
models (LLMs) and lists of hypotheses generated by an ASR to address the CS
problem. Specifically, we first employ multiple well-trained ASR models for
N-best hypotheses generation, with the aim of increasing the diverse and
informative elements in the set of hypotheses. Next, we utilize the LLMs to
learn the hypotheses-to-transcription (H2T) mapping by adding a trainable
low-rank adapter. Such a generative error correction (GER) method directly
predicts the accurate transcription according to its expert linguistic
knowledge and N-best hypotheses, resulting in a paradigm shift from the
traditional language model rescoring or error correction techniques.
Experimental evidence demonstrates that GER significantly enhances CS-ASR
accuracy, in terms of reduced mixed error rate (MER). Furthermore, LLMs show
remarkable data efficiency for H2T learning, providing a potential solution to
the data scarcity problem of CS-ASR in low-resource languages.
