# Enhancing Code-switching Speech Recognition with Interactive Language Biases

arXiv: https://arxiv.org/abs/2309.16953 · HTML: https://mlsanity.com/paper/2309.16953/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2023.

## Built on by (most cited first, 21 of 21)

- [Mamba in Speech: Towards an Alternative to Self-Attention](https://mlsanity.com/paper/2405.12609/) (2024)
- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition](https://mlsanity.com/paper/2402.05457/) (2024)
- [Rethinking Mamba in Speech Processing by Self-Supervised Models](https://mlsanity.com/paper/2409.07273/) (2024)
- [PIER: A Novel Metric for Evaluating What Matters in Code-Switching](https://mlsanity.com/paper/2501.09512/) (2025)
- [Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding](https://mlsanity.com/paper/2412.16507/) (2024)
- [Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data](https://mlsanity.com/paper/2407.10603/) (2024)
- [Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languages](https://mlsanity.com/paper/2506.14177/) (2025)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition](https://mlsanity.com/paper/2412.12760/) (2024)
- [Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical Routing](https://mlsanity.com/paper/2407.18581/) (2024)
- [AdaCS: Adaptive Normalization for Enhanced Code-Switching ASR](https://mlsanity.com/paper/2501.07102/) (2025)
- [UniCoM: A Universal Code-Switching Speech Generator](https://mlsanity.com/paper/2508.15244/) (2025)
- [Improving Code-Switching Speech Recognition with TTS Data Augmentation](https://mlsanity.com/paper/2601.00935/) (2026)
- [Direct Preference Optimization for English-Mandarin Code-Switching Speech Recognition in Audio LLMs](https://mlsanity.com/paper/2605.23975/) (2026)
- [CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition](https://mlsanity.com/paper/2502.01777/) (2025)
- [Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech](https://mlsanity.com/paper/2606.19381/) (2026)
- [Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection](https://mlsanity.com/paper/2412.08651/) (2024)
- [EM2LDL: A Multilingual Speech Corpus for Mixed Emotion Recognition through Label Distribution Learning](https://mlsanity.com/paper/2511.20106/) (2025)
- [LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR](https://mlsanity.com/paper/2610.16579/) (2026)

## Abstract (arXiv)

Languages usually switch within a multilingual speech signal, especially in a bilingual society. This phenomenon is referred to as code-switching (CS), making automatic speech recognition (ASR) challenging under a multilingual scenario. We propose to improve CS-ASR by biasing the hybrid CTC/attention ASR model with multi-level language information comprising frame- and token-level language posteriors. The interaction between various resolutions of language biases is subsequently explored in this work. We conducted experiments on datasets from the ASRU 2019 code-switching challenge. Compared to the baseline, the proposed interactive language biases (ILB) method achieves higher performance and ablation studies highlight the effects of different language biases and their interactions. In addition, the results presented indicate that language bias implicitly enhances internal language modeling, leading to performance degradation after employing an external language model.