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CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition

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arxiv 2502.18913 v2 pith:JT3PJSFR submitted 2025-02-26 cs.CL cs.SDeess.AS

CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition

Jiaming Zhou , Yujie Guo , Shiwan Zhao , Haoqin Sun , Hui Wang , Jiabei He , Aobo Kong , Shiyao Wang
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Xi Yang Yequan Wang Yonghua Lin Yong Qin
This is my paper
classification cs.CL cs.SDeess.AS
keywords code-switchingcs-dialoguedatasetspeechmandarin-englishmodelschallengesdatasets
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Code-switching (CS), the alternation between two or more languages within a single conversation, presents significant challenges for automatic speech recognition (ASR) systems. Existing Mandarin-English code-switching datasets often suffer from limitations in size, spontaneity, and the lack of full-length dialogue recordings with transcriptions, hindering the development of robust ASR models for real-world conversational scenarios. This paper introduces CS-Dialogue, a novel large-scale Mandarin-English code-switching speech dataset comprising 104 hours of spontaneous conversations from 200 speakers. Unlike previous datasets, CS-Dialogue provides full-length dialogue recordings with complete transcriptions, capturing naturalistic code-switching patterns in continuous speech. We describe the data collection and annotation processes, present detailed statistics of the dataset, and establish benchmark ASR performance using state-of-the-art models. Our experiments, using Transformer, Conformer, and Branchformer, demonstrate the challenges of code-switching ASR, and show that existing pre-trained models such as Whisper still have the space to improve. The CS-Dialogue dataset will be made freely available for all academic purposes.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs

    cs.CL 2025-12 unverdicted novelty 7.0

    Cascaded systems remain the most reliable for speech translation overall, but recent SpeechLLMs match or outperform them in many conditions while standalone speech models lag.

  2. Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation

    cs.AI 2026-05 unverdicted novelty 6.0

    Agentic ASR adds closed-loop semantic correction to ASR and introduces S²ER, an LLM judge for meaning-level errors, showing larger gains on semantic than token metrics across multilingual benchmarks.

  3. Rethinking Entropy Allocation in LLM-based ASR: Understanding the Dynamics between Speech Encoders and LLMs

    eess.AS 2026-04 unverdicted novelty 6.0

    A multi-stage training method for LLM-based ASR uses new entropy allocation metrics to achieve competitive benchmark performance with 2.3B parameters while mitigating hallucinations via better encoder-LLM decoupling.

  4. Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs

    cs.CL 2026-06 unverdicted novelty 5.0

    Merged bilingual CS-ASR models show only modest generalization to unseen language pairs, indicating limited transfer of code-switching capabilities.

  5. A Unified and Reproducible Experimentation Framework for Speech Understanding

    eess.AS 2026-05 unverdicted novelty 5.0

    SURE is a new standardized framework for evaluating and training speech foundation models and Speech LLMs to improve comparability and reproducibility under realistic conditions.

  6. Direct Preference Optimization for English-Mandarin Code-Switching Speech Recognition in Audio LLMs

    cs.CL 2026-05 unverdicted novelty 4.0

    DPO on three Audio LLMs using 100K preference pairs yields up to 89.6% in-distribution and 20.0% out-of-distribution MER reduction for code-switching transcription.

  7. NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR

    eess.AS 2026-04 conditional novelty 4.0

    A 2.3B-parameter LLM-based ASR system achieves competitive recognition accuracy and reduced hallucination through a multi-stage training paradigm with asynchronous encoder updates, ASR-specialized RL, and phoneme-leve...

  8. NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR

    eess.AS 2026-04 unverdicted novelty 4.0

    NIM4-ASR delivers SOTA ASR performance on public benchmarks using a 2.3B-parameter LLM with multi-stage training, real-time streaming, and million-scale hotword customization via RAG.