# Learning ASR-Robust Contextualized Embeddings for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1909.10861 · HTML: https://mlsanity.com/paper/1909.10861/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 20 of 20)

- [Innovative Bert-based Reranking Language Models for Speech Recognition](https://mlsanity.com/paper/2104.04950/) (2021)
- [ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding](https://mlsanity.com/paper/2108.13048/) (2021)
- [PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling](https://mlsanity.com/paper/2208.11508/) (2022)
- [ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction](https://mlsanity.com/paper/2310.05129/) (2023)
- [Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning](https://mlsanity.com/paper/2104.10357/) (2021)
- [ASR Error Correction with Constrained Decoding on Operation Prediction](https://mlsanity.com/paper/2208.04641/) (2022)
- [Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations](https://mlsanity.com/paper/2310.03518/) (2023)
- [DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task](https://mlsanity.com/paper/2310.10169/) (2023)
- [Multimodal Audio-textual Architecture for Robust Spoken Language Understanding](https://mlsanity.com/paper/2306.06819/) (2023)
- [Enhancing Dialogue Speech Recognition with Robust Contextual Awareness via Noise Representation Learning](https://mlsanity.com/paper/2408.06043/) (2024)
- [Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection](https://mlsanity.com/paper/2205.11008/) (2022)
- [Speak & Spell: LLM-Driven Controllable Phonetic Error Augmentation for Robust Dialogue State Tracking](https://mlsanity.com/paper/2409.06263/) (2024)
- [Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding](https://mlsanity.com/paper/2410.15609/) (2024)
- [PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decoding](https://mlsanity.com/paper/2506.11064/) (2025)
- [TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild](https://mlsanity.com/paper/2603.21478/) (2026)
- [Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with Subwords](https://mlsanity.com/paper/2102.02270/) (2021)
- [Improve Text Classification Accuracy with Intent Information](https://mlsanity.com/paper/2212.07649/) (2022)
- [Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding](https://mlsanity.com/paper/2405.15097/) (2024)
- [Graph-Based Phonetic Error Correction of Noisy ASR](https://mlsanity.com/paper/2606.24889/) (2026)
- [Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts](https://mlsanity.com/paper/2609.00330/) (2026)

## Abstract (arXiv)

Employing pre-trained language models (LM) to extract contextualized word representations has achieved state-of-the-art performance on various NLP tasks. However, applying this technique to noisy transcripts generated by automatic speech recognizer (ASR) is concerned. Therefore, this paper focuses on making contextualized representations more ASR-robust. We propose a novel confusion-aware fine-tuning method to mitigate the impact of ASR errors to pre-trained LMs. Specifically, we fine-tune LMs to produce similar representations for acoustically confusable words that are obtained from word confusion networks (WCNs) produced by ASR. Experiments on the benchmark ATIS dataset show that the proposed method significantly improves the performance of spoken language understanding when performing on ASR transcripts. Our source code is available at https://github.com/MiuLab/SpokenVec