| Challenge: | Adapting a model to target speakers requires a lot of compute and may cause catastrophic forgetting to the existing speakers. |
| Approach: | They propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation. |
| Outcome: | The proposed model outperforms baseline models with 20.58% relative WER reduction and surpasses finetuning method by 2.54% on target speaker adaptation. |
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| Challenge: | Automatic Speech Recognition systems perform poorly on atypical speech and heavily accented speech. |
| Approach: | They add a residual adapter to the encoder layer to improve model adaptation . they show that the residual adapters update only a tiny fraction of the model parameters . |
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Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)
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| 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. |
Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models (2023.emnlp-main)
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| Challenge: | Existing pruning strategies struggle to enhance robustness against adversarial attacks when continually increasing model sparsity and require a retraining process. |
| Approach: | They propose a pruning strategy that replicates embedding space and feature space of dense language models and aims to conserve more pre-trained knowledge during the pruning process. |
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Adapting Where It Matters: Depth-Aware Adaptation for Efficient Multilingual Speech Recognition in Low-Resource Languages (2026.findings-acl)
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| Challenge: | Recent speech foundation models excel at multilingual automatic speech recognition (ASR) for high-resource languages, but their performance drops substantially on low-resourced languages due to the limited data availability. |
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Advancing African-Accented English Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models (2025.acl-srw)
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| Challenge: | Accents play a pivotal role in shaping human communication, a new study finds . existing ASR systems often perform inadequately, even mispronouncing African names . |
| Approach: | They propose a method that uses epistemic uncertainty to automate annotation to reduce costs and human labor. |
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SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026.acl-long)
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Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz Tandazo, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Éric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar-Corrales, null Surya, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux
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| Approach: | They propose a bi-level optimization framework for rapid adaptation of speech units to new languages using minimal unlabeled data. |
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Speech-enriched Memory for Inference-time Adaptation of ASR Models to Word Dictionaries (2023.emnlp-main)
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| Challenge: | Existing contextual biasing techniques require additional parameterization . state-of-the-art ASR systems often fail to recognize named entities or critical rare words . |
| Approach: | They propose an algorithm that uses nearest-neighbor matching to predict ASR models . a list of rare entities is indexed in a memory and then stored the best possible match . |
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Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models (L18-1)
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| Challenge: | Existing speaker adaptation algorithms for BLSTM-CTC AMs are lacking . TED-LIUM corpus shows speaker adaptation provides 11-20% word error rate reduction over baseline model built on raw filter-bank features. |
| Approach: | They propose to use feature-space adaptation techniques for bidirectional long short term memory (BLSTM) recurrent neural network based acoustic models trained with the connectionist temporal classification objective function to improve speaker adaptation. |
| Outcome: | The proposed approach provides up to 11-20% of word error reduction over baseline models on the TED-LIUM corpus. |
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech (D19-3)
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| Challenge: | Language model adaptation (LMA) is a promising solution for conversational speech recognition systems. |
| Approach: | They propose to use language model adaptation techniques to adapt language models to conversational speech recognition. |
| Outcome: | The proposed toolkit compares state-of-the-art language model adaptation techniques in conversational speech recognition tasks. |
Advancing Test-Time Adaptation in Wild Acoustic Test Settings (2024.emnlp-main)
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| Challenge: | Existing wild vision TTA methods fail to handle speech data due to the unique characteristics of high-entropy speech frames, which are unreliably filtered out even when containing crucial semantic content. |
| Approach: | They propose a method for acoustic foundation models to perform confidence-based adaptation in wild acustic test settings. |
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