mlsanityData to 7 Oct 2026

XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition

arXiv 2408.10524 ↗First posted 20 Aug 2024cs.CL cs.AI cs.SDcited by 0

Not on the map. Why?

Abstract (arXiv)

Contextualized ASR models have been demonstrated to effectively improve the recognition accuracy of uncommon phrases when a predefined phrase list is available. However, these models often struggle with bilingual settings, which are prevalent in code-switching speech recognition. In this study, we make the initial attempt to address this challenge by introducing a Cross-lingual Contextual Biasing(XCB) module. Specifically, we augment a pre-trained ASR model for the dominant language by integrating an auxiliary language biasing module and a supplementary language-specific loss, aimed at enhancing the recognition of phrases in the secondary language. Experimental results conducted on our in-house code-switching dataset have validated the efficacy of our approach, demonstrating significant improvements in the recognition of biasing phrases in the secondary language, even without any additional inference overhead. Additionally, our proposed system exhibits both efficiency and generalization when is applied by the unseen ASRU-2019 test set.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 18 references that are in the map. First posted on arXiv 20 Aug 2024.

Builds on

14 of 18 references.

References · 14

  1. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
  2. 2015Attention-Based Models for Speech Recognitioncited by 400
  3. 2022Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognitioncited by 139
  4. 2018Deep context: end-to-end contextual speech recognitioncited by 136
  5. 2017An analysis of incorporating an external language model into a sequence-to-sequence modelcited by 109
  6. 2020Multi-Encoder-Decoder Transformer for Code-Switching Speech Recognitioncited by 28
  7. 2019Self-Attention Transducers for End-to-End Speech Recognitioncited by 24
  8. 2020Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognitioncited by 23
  9. 2022LAE: Language-Aware Encoder for Monolingual and Multilingual ASRcited by 21
  10. 2021Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorizationcited by 17
  11. 2023SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Abilitycited by 16
  12. 2023Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognitioncited by 12
2 more
  1. 2023BA-MoE: Boundary-Aware Mixture-of-Experts Adapter for Code-Switching Speech Recognitioncited by 8
  2. 2024An efficient text augmentation approach for contextualized Mandarin speech recognitioncited by 2

Built on by

Not cited yet.