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Accented Speech Recognition: A Survey

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arxiv 2104.10747 v2 pith:QK6KK6E6 submitted 2021-04-21 cs.CL cs.SDeess.AS

Arthur Hinsvark (1) , Natalie Delworth (1) , Miguel Del Rio (1) , Quinten McNamara (1) , Joshua Dong (1) , Ryan Westerman (1) , Michelle Huang (1) , Joseph Palakapilly (1)
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Jennifer Drexler (1) Ilya Pirkin (1) Nishchal Bhandari (1) Miguel Jette (1) ((1) Rev.com)
This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords speechaccentedchallengesrecognitionaccentsapproachessurveysystems
verification ladder T0 review T1 audit T2 compute T3 formal
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Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR. We present a survey of current promising approaches to accented speech recognition and highlight the key challenges in the space. Approaches mostly focus on single model generalization and accent feature engineering. Among the challenges, lack of a standard benchmark makes research and comparison especially difficult.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 15 citations worldwide. Full citation record

  1. Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR

    cs.CL 2025-06 conditional novelty 7.0 of 10

    For a fixed training budget, low-resource ASR models handle unseen accents best when trained on more speakers with less audio each, and accent diversity in training gives minimal extra benefit.

  2. Discrete Tokens Exhibit Interlanguage Speech Intelligibility Benefit: an Analytical Study Towards Accent-robust ASR Only with Native Speech Data

    cs.SD 2025-05 conditional novelty 6.0 of 10

    Discrete speech tokens trained on a speaker's first language improve ASR accuracy on that speaker's accented English, reproducing the interlanguage speech intelligibility benefit in machines.

  3. A Tactical Behaviour Recognition Framework Based on Causal Multimodal Reasoning: A Study on Covert Audio-Video Analysis Combining GAN Structure Enhancement and Phonetic Accent Modelling

    cs.CY 2025-07 reject novelty 2.0 of 10

    TACTIC-GRAPHS is a proposed multimodal graph reasoning framework for tactical threat detection; performance claims are unsupported and internally inconsistent.

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