Sep 2026· Journal of Ambient Intelligence and Humanized Computing· 1 citation
Speech Recognition and SynthesisNatural Language Processing Techniques
Abstract
Abstract Code-switching, the alternation between two or more languages within communication, poses great challenges for Automatic Speech Recognition (ASR) systems. Existing models and datasets are limited in their ability to effectively handle these challenges. To address this gap and foster progress in code-switching ASR research, we introduce the DOTA-ME-CS: Daily oriented text audio Mandarin-English code-switching dataset, which consists of 18.54 h of audio data, including 9300 recordings from 34 participants. To enhance the dataset’s diversity, we apply artificial intelligence (AI) techniques such as AI timbre synthesis, speed variation, and noise addition, thereby increasing the complexity and scalability of the task. The dataset is carefully curated to ensure both diversity and quality, providing a robust resource for researchers addressing the intricacies of bilingual speech recognition with detailed data analysis. We further demonstrate the dataset’s potential in future research. The DOTA-ME-CS dataset, Along with accompanying code are in: https://github.com/zifanwei/asr-code-switch.
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