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

• URL: http://arxiv.org/abs/2502.18913v2
• Date: Wed, 12 Mar 2025 03:06:01 GMT
• Title: CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition
• Authors: Jiaming Zhou, Yujie Guo, Shiwan Zhao, Haoqin Sun, Hui Wang, Jiabei He, Aobo Kong, Shiyao Wang, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin,
• Abstract summary: Code-switching is the alternation between two or more languages within a single conversation.<n>Existing Mandarin-English code-switching datasets often suffer from limitations in size, spontaneity, and the lack of full-length dialogue recordings with transcriptions.<n>This paper introduces CS-Dialogue, a novel large-scale Mandarin-English code-switching speech dataset comprising 104 hours of spontaneous conversations from 200 speakers.
• Score: 17.806195208457428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition [48.527630771422935]
  We propose a synthetic data generation pipeline for multi-speaker conversational ASR. We conduct evaluation by fine-tuning the Whisper ASR model for telephone and distant conversational speech settings.
  arXiv  Detail & Related papers  (2024-08-17T14:47:05Z)
• Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation [46.93969003104427]
  This paper introduces an extensive speech-text LLM framework, the Unified Spoken Dialog Model (USDM) USDM is designed to generate coherent spoken responses with naturally occurring prosodic features relevant to the given input speech. Our approach effectively generates natural-sounding spoken responses, surpassing previous and cascaded baselines.
  arXiv  Detail & Related papers  (2024-02-08T14:35:09Z)
• Leveraging Language ID to Calculate Intermediate CTC Loss for Enhanced Code-Switching Speech Recognition [5.3545957730615905]
  We introduce language identification information into the middle layer of the ASR model's encoder. We aim to generate acoustic features that imply language distinctions in a more implicit way, reducing the model's confusion when dealing with language switching.
  arXiv  Detail & Related papers  (2023-12-15T07:46:35Z)
• Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching [65.74653592668743]
  Finetuning self-supervised multilingual representations reduces absolute word error rates by up to 20%. In circumstances with limited training data finetuning self-supervised representations is a better performing and viable solution.
  arXiv  Detail & Related papers  (2023-11-25T17:05:21Z)
• Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation [27.926862030684926]
  We introduce a novel conversational ASR system, extending the Conformer encoder-decoder model with cross-modal conversational representation. Our approach combines pre-trained speech and text models through a specialized encoder and a modal-level mask input. By introducing both cross-modal and conversational representations into the decoder, our model retains context over longer sentences without information loss.
  arXiv  Detail & Related papers  (2023-10-22T11:57:33Z)
• Speech collage: code-switched audio generation by collaging monolingual corpora [50.356820349870986]
  Speech Collage is a method that synthesizes CS data from monolingual corpora by splicing audio segments. We investigate the impact of generated data on speech recognition in two scenarios.
  arXiv  Detail & Related papers  (2023-09-27T14:17:53Z)
• Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer [17.700515986659063]
  Code-Switching (CS) multilingual Automatic Speech Recognition (ASR) models can transcribe speech containing two or more alternating languages during a conversation. This paper proposes a new method for creating code-switching ASR datasets from purely monolingual data sources. A novel Concatenated Tokenizer enables ASR models to generate language ID for each emitted text token while reusing existing monolingual tokenizers.
  arXiv  Detail & Related papers  (2023-06-14T21:24:11Z)
• SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents [72.42049370297849]
  SpokenWOZ is a large-scale speech-text dataset for spoken TOD. Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
  arXiv  Detail & Related papers  (2023-05-22T13:47:51Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)
• Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models [55.82292352607321]
  Code-switching (CS) is common in daily conversations where more than one language is used within a sentence. This paper uses the recently successful self-supervised learning (SSL) methods to leverage many unlabeled speech data without CS.
  arXiv  Detail & Related papers  (2021-10-07T14:43:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.