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

• URL: http://arxiv.org/abs/2606.05846v1
• Date: Thu, 04 Jun 2026 08:22:55 GMT
• Title: Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs
• Abstract summary: Code-switching ASR (CS-ASR) remains particularly challenging due to the severe scarcity of multilingual CS speech resources.<n>Existing approaches improve CS-ASR performance through synthetic CS speech generation or pair-specific fine-tuning.<n>We investigate whether CS capabilities learned from a limited set of seen language pairs can generalize to unseen language pairs.
• Score: 4.697631271009118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic Speech Recognition (ASR) has become a key technology for human--AI interaction. However, code-switching ASR (CS-ASR) remains particularly challenging due to the severe scarcity of multilingual CS speech resources across diverse language pairs. Existing approaches primarily improve CS-ASR performance through synthetic CS speech generation or pair-specific fine-tuning on limited bilingual datasets. Nevertheless, these approaches face an inherent scalability limitation, as support for CS must be developed separately for language pairs whose number grows combinatorially with the number of supported languages. In this work, we investigate whether CS capabilities learned from a limited set of seen language pairs can generalize to unseen language pairs through model merging and domain generalization methods. Our experiments show that merged bilingual CS-ASR models modestly generalize to unseen language pairs, suggesting limited transfer of bilingual CS capabilities across language pairs.

Related papers

• Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages [76.14451035425229]
  We introduce Omnilingual ASR, a large-scale automatic speech recognition system.<n>It scales self-supervised pre-training to 7B parameters to learn robust speech representations.<n>It expands coverage to over 1,600 languages, including over 500 never before served by ASR.
  arXiv  Detail & Related papers  (2025-11-12T19:48:09Z)
• UniCoM: A Universal Code-Switching Speech Generator [19.893429976826464]
  Code-Switching (CS), the alternation between two or more languages within a single speaker's utterances, is common in real-world conversations.<n>We propose Universal Code-Mixer (UniCoM), a novel pipeline for generating high-quality, natural CS samples.<n>We construct Code-Switching FLEURS (CS-FLEURS), a multilingual CS corpus designed for automatic speech recognition (ASR) and speech-to-text translation (S2TT)
  arXiv  Detail & Related papers  (2025-08-21T05:11:21Z)
• Optimizing ASR for Catalan-Spanish Code-Switching: A Comparative Analysis of Methodologies [9.224033819309708]
  Code-switching (CS), the alternating use of two or more languages, challenges automatic speech recognition (ASR) due to scarce training data and linguistic similarities.<n>We improve ASR for Catalan-Spanish CS by exploring three strategies: (1) generating synthetic CS data, (2) concatenating monolingual audio, and (3) leveraging real CS data with language tokens.<n>Results show that combining a modest amount of synthetic CS data with the dominant language token yields the best transcription performance.
  arXiv  Detail & Related papers  (2025-07-18T12:54:41Z)
• Efficient Multilingual ASR Finetuning via LoRA Language Experts [59.27778147311189]
  This paper proposes an efficient finetuning framework for customized multilingual ASR via prepared LoRA language experts based on Whisper.<n>Through LoRA expert fusion or knowledge distillation, our approach achieves better recognition performance on target languages than standard fine-tuning methods.<n> Experimental results demonstrate that the proposed models yield approximately 10% and 15% relative performance gains in language-aware and language-agnostic scenarios.
  arXiv  Detail & Related papers  (2025-06-11T07:06:27Z)
• Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling [50.62091603179394]
  Whisper, one of the most advanced ASR models, handles 99 languages effectively.<n>However, Whisper struggles with unseen languages, those not included in its pre-training.<n>We propose methods that exploit these relationships to enhance ASR performance on unseen languages.
  arXiv  Detail & Related papers  (2024-12-21T04:05:43Z)
• Enhancing Code-switched Text-to-Speech Synthesis Capability in Large Language Models with only Monolingual Corpora [13.891322931352649]
  We propose a Code-Switched Large Language Model (CS-LLM) to enhance the code-switched text-to-speech synthesis (CS TTS) capability.<n>Specifically, we begin by enhancing the multilingual speech processing ability of LLMs through multilingual speech recognition and synthesis tasks.<n>We develop an effective code-switched (CS) data construction strategy that splits and splits words from different monolingual speech corpora to equip LLMs with improved CS TTS ability.
  arXiv  Detail & Related papers  (2024-09-17T08:11:07Z)
• Cross-Modal Mutual Learning for Cued Speech Recognition [10.225972737967249]
  We propose a transformer based cross-modal mutual learning framework to prompt multi-modal interaction. Our model forces modality-specific information of different modalities to pass through a modality-invariant codebook. We establish a novel large-scale multi-speaker CS dataset for Mandarin Chinese.
  arXiv  Detail & Related papers  (2022-12-02T10:45:33Z)
• LAE: Language-Aware Encoder for Monolingual and Multilingual ASR [87.74794847245536]
  A novel language-aware encoder (LAE) architecture is proposed to handle both situations by disentangling language-specific information. Experiments conducted on Mandarin-English code-switched speech suggest that the proposed LAE is capable of discriminating different languages in frame-level.
  arXiv  Detail & Related papers  (2022-06-05T04:03:12Z)
• 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)
• Style Variation as a Vantage Point for Code-Switching [54.34370423151014]
  Code-Switching (CS) is a common phenomenon observed in several bilingual and multilingual communities. We present a novel vantage point of CS to be style variations between both the participating languages. We propose a two-stage generative adversarial training approach where the first stage generates competitive negative examples for CS and the second stage generates more realistic CS sentences.
  arXiv  Detail & Related papers  (2020-05-01T15:53:16Z)

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.