{"ID":2863317,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24310","arxiv_id":"2509.24310","title":"Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives","abstract":"Code-switching automatic speech recognition (CS-ASR) presents unique challenges due to language confusion introduced by spontaneous intra-sentence switching and accent bias that blurs the phonetic boundaries. Although the constituent languages may be individually high-resource, the scarcity of annotated code-switching data further compounds these challenges. In this paper, we systematically analyze CS-ASR from both model-centric and data-centric perspectives. By comparing state-of-the-art algorithmic methods, including language-specific processing and auxiliary language-aware multi-task learning, we discuss their varying effectiveness across datasets with different linguistic characteristics. On the data side, we first investigate TTS as a data augmentation method. By varying the textual characteristics and speaker accents, we analyze the impact of language confusion and accent bias on CS-ASR. To further mitigate data scarcity and enhance textual diversity, we propose a prompting strategy by simplifying the equivalence constraint theory (SECT) to guide large language models (LLMs) in generating linguistically valid code-switching text. The proposed SECT outperforms existing methods in ASR performance and linguistic quality assessments, generating code-switching text that more closely resembles real-world code-switching text. When used to generate speech-text pairs via TTS, SECT proves effective in improving CS-ASR performance. Our analysis of both model- and data-centric methods underscores that effective CS-ASR requires strategies to be carefully aligned with the specific linguistic characteristics of the code-switching data.","short_abstract":"Code-switching automatic speech recognition (CS-ASR) presents unique challenges due to language confusion introduced by spontaneous intra-sentence switching and accent bias that blurs the phonetic boundaries. Although the constituent languages may be individually high-resource, the scarcity of annotated code-switching...","url_abs":"https://arxiv.org/abs/2509.24310","url_pdf":"https://arxiv.org/pdf/2509.24310v2","authors":"[\"Hexin Liu\",\"Haoyang Zhang\",\"Qiquan Zhang\",\"Xiangyu Zhang\",\"Dongyuan Shi\",\"Eng Siong Chng\",\"Haizhou Li\"]","published":"2025-09-29T05:46:05Z","proceeding":"eess.AS","tasks":"[\"eess.AS\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}