No Label? No Problem: Unsupervised Continual Learning for Adaptive Medical ASR (2026.eacl-industry)
Copied to clipboard
| Challenge: | Medical audio often contains specialized terminology, such as medication names, which existing ASR systems struggle to transcribe accurately. |
| Approach: | They propose an unsupervised continual learning ASR framework that adapts to new data while preserving prior knowledge. |
| Outcome: | Experiments on real-world medical audio show that the proposed framework improves over state-of-the-art models. |
Similar Papers
Simple and Effective Unsupervised Speech Translation (2023.acl-long)
Copied to clipboard
Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
| Approach: | They propose a pipeline approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis. |
| Outcome: | The proposed approach outperforms the state-of-the-art in unsupervised speech recognition by 3.2 BLEU on the Libri-Trans benchmark and the best supervised end-to-end models from only two years ago by an average of 5.0 BLUE over five X-En directions. |
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current ASR TTA methods focus on non-continual TTA, which limits cross-sample knowledge learning compared to continual TTA. |
| Approach: | They propose a Fast-slow TTA framework that leverages the advantage of continual and non-continual TTA and a Dynamic SUTA method that automatically detects domain shifts and resets the model. |
| Outcome: | The proposed method outperforms non-continual and continual TTA methods while maintaining robustness to domain shifts without requiring domain boundary information. |
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder (2025.acl-industry)
Copied to clipboard
Khai Le-Duc, Phuc Phan, Tan-Hanh Pham, Bach Phan Tat, Minh-Huong Ngo, Thanh Nguyen-Tang, Truong-Son Hy
| Challenge: | Multilingual automatic speech recognition (ASR) in the medical domain is a critical foundational task, serving a wide range of downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. |
| Approach: | They present the first multilingual medical ASR dataset and the first collection of small-to-large end-to end medical APR models spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese. |
| Outcome: | The proposed model covers Vietnamese, English, German, French, and Mandarin Chinese, and is the first multilingual ASR dataset across five languages. |
Synthetic Doctor-Patient Dialogue Generation for Robust Medical ASR: A Scalable Pipeline for Vocabulary Expansion and Privacy Preservation (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing ASR models struggle with high word error rates (WER) on clinical vocabulary, especially medication names. |
| Approach: | They propose to generate doctor-patient dialogues in both text and audio formats using a curated set of over 124,000 medical terms. |
| Outcome: | The proposed pipeline generated over 1 billion audios with ground truth transcriptions. |
Semi-Supervised Diseased Detection from Speech Dialogues with Multi-Level Data Modeling (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for learning from weakly-supervised speech data are hampered by severe data scarcity and the subjective nature of clinical annotations. |
| Approach: | They propose a framework that explicitly models pathological traits by jointly learning from frame-level, segment-level and session-level representations within unsegmented clinical dialogues. |
| Outcome: | The proposed framework is model-agnostic, robust across languages and conditions, and highly data-efficient. |
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale (2022.emnlp-industry)
Copied to clipboard
Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey, Yajie Mao, Vladislav Belyaev, Madhuri Emmadi, Craig Murray, Ferhan Ture, Jimmy Lin
| Challenge: | End-to-end automatic speech recognition systems require thousands of hours of manual annotation and heavyweight computation to perform inference. |
| Approach: | They propose to use a third-party ASR system as a weak supervision source and labeling functions derived from implicit user feedback to reduce human labor. |
| Outcome: | The proposed system improves word-error rate and speed up 600% over third-party ASR. |
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
Copied to clipboard
| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
| Outcome: | The proposed techniques are more challenging yet widely applicable. |
Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts. |
| Approach: | They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios. |
| Outcome: | Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines. |
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2022.naacl-main)
Copied to clipboard
| Challenge: | Pretrained language models are typically learned over a large, static corpus and fine-tuned for various downstream tasks. |
| Approach: | They propose to continuously update a pretrained language model to adapt to emerging data and to keep track of the model's performance. |
| Outcome: | The proposed model can adapt to new corpora while retaining knowledge in earlier domains. |
Noise-Robust Training with Dynamic Loss and Contrastive Learning for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a task in natural language processing that aims at locating entity mentions in a given sentence and assigning them to certain types. |
| Approach: | They propose to use a dynamic loss function to better adapt to the changing noise during the training process and incorporate token level contrastive learning to fully utilize the noisy data. |
| Outcome: | The proposed method outperforms existing NER models on three benchmark datasets and outperformed existing models by significant margins. |