| Challenge: | Degradation in performance across underrepresented accents is a severe deterrent to inclusive adoption of ASR. |
| Approach: | They propose an approach to adapt speech accents to unseen accents by using cross-attention with a trainable set of codebooks. |
| Outcome: | The proposed approach yields significant performance gains on the seen English accents and unseen accents on the Mozilla Common Voice dataset. |
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AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition (2020.lrec-1)
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| Challenge: | aaron e. sanchez and joe saunders: automatic speech recognition still faces a major challenge . they say accents are a way of pronouncing a language, and speakers always have manner of speaking . esassen: accents can be used to identify non-native speakers of a speech . |
| Approach: | They propose to create a database of speech samples in non-native accents for ASR testing . they also propose to introduce accent neutralization of non- native accents to native accent . |
| Outcome: | The proposed model is compared against human-labelled accent classes and is generalized against human data. |
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. |
| Outcome: | The proposed method reduces costs and human labor by reducing data annotation and epistemic uncertainty. |
How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems (2020.acl-main)
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| Challenge: | a new study examines how accent information is encoded and propagated in an end-to-end ASR system. |
| Approach: | They propose to use phone probes to analyze phonetic content of representations at each layer. |
| Outcome: | The proposed model is based on a large amount of US-accented English speech and is compared with other models using phone probes. |
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents (2024.findings-eacl)
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| Challenge: | AccentFold uses spatial relationships to improve speech recognition for accented speech . existing methods for accent recognition have been limited due to data scarcity and budget constraints . |
| Approach: | They propose a method that exploits spatial relationships between learned accent embeddings to improve downstream automatic speech recognition. |
| Outcome: | The proposed method outperforms baseline methods in accented speech training. |
A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
| Outcome: | The proposed models can be used to improve performance on low-resource languages by using unlabeled speech data. |
Evaluation of Off-the-shelf Speech Recognizers on Different Accents in a Dialogue Domain (2022.lrec-1)
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| Challenge: | Existing automatic speech recognition systems for non-American accents have a much higher error rate than for general american accents. |
| Approach: | They evaluate automatic speech recognition systems on agent-directed speech . they find that the performance is worse for non-American accents than for General American . |
| Outcome: | The ASR systems perform worse for non-American accents than for General American accents . the results suggest that training on non-native English speakers is needed to narrow the performance gap. |
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach (L18-1)
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| Challenge: | Automated Speech Recognition systems degrade in performance when recognizing accents that are different from the ones in training data. |
| Approach: | They propose to adapt Acoustic Models that are trained on one accent to a target accent by using a small amount of speech data in the target accent. |
| Outcome: | The proposed model can be used to identify accents in Indian English and other languages. |
Mixture-of-Experts with Intermediate CTC Supervision for Accented Speech Recognition (2026.acl-long)
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| Challenge: | Accented speech remains a persistent challenge for automatic speech recognition (ASR) Accent-agnostic approaches improve robustness but struggle with heavily accented or unseen varieties . |
| Approach: | They propose a Mixture-of-Experts architecture with intermediate CTC supervision that promotes expert specialization and generalization. |
| Outcome: | Experiments show that the proposed architecture improves on accented speech . the proposed framework is based on a mixture-of-experts architecture with intermediate supervision . |
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech (2021.emnlp-main)
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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 . |
| Outcome: | The proposed model fine-tuning improves performance on atypical and accented speech . the system can update only a tiny fraction of the model parameters . |
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder (2025.acl-industry)
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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. |