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.

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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 .
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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 .
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On Construction of the ASR-oriented Indian English Pronunciation Dictionary (2020.lrec-1)

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Challenge: Indian English (IE) has distinctive characteristics, especially phonologically, from other varieties of English.
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BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources (2026.acl-long)

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Challenge: Existing reviews focus on a few high-resource languages or embed Indian languages within broad multilingual settings, limiting coverage of low-resourced and culturally diverse varieties.
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Evaluating Open-Source ASR Systems: Performance Across Diverse Audio Conditions and Error Correction Methods (2025.coling-main)

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Challenge: Automated speech recognition (ASR) systems are able to transcribe spontaneous human conversations with high accuracy.
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Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights (2021.findings-acl)

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Challenge: In this paper, we propose the first large scale study of automatic speech recognition in Sanskrit . we focus on the impact of unit selection in San's ASR systems .
Approach: They propose a large scale study of automatic speech recognition in Sanskrit . they propose syllable level unit selection that captures character sequences .
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Language-specific Effects on Automatic Speech Recognition Errors for World Englishes (2022.coling-1)

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Challenge: Existing systems are not able to meet the needs of speakers of different demographic groups.
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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.
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An (unhelpful) guide to selecting the best ASR architecture for your under-resourced language (2023.acl-short)

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Challenge: English ASR now has word error rates comparable to that of human transcriptionists, but only for the handful of the world's 7000 languages with abundant training resources.
Approach: They propose to use four of the most popular ASR toolkits to train ASR models for eleven languages with limited ASR training resources: eleven widely spoken languages of Africa, Asia, and South America, one endangered language of Central America, and three critically endangered languages of North America.
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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 .
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