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- W4287829027 abstract "Local dialects influence people to pronounce words of the same language differently from each other. The great variability and complex characteristics of accents creates a major challenge for training a robust and accent-agnostic automatic speech recognition (ASR) system. In this paper, we introduce a cross-accented English speech recognition task as a benchmark for measuring the ability of the model to adapt to unseen accents using the existing CommonVoice corpus. We also propose an accent-agnostic approach that extends the model-agnostic meta-learning (MAML) algorithm for fast adaptation to unseen accents. Our approach significantly outperforms joint training in both zero-shot, few-shot, and all-shot in the mixed-region and cross-region settings in terms of word error rate." @default.
- W4287829027 created "2022-07-26" @default.
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- W4287829027 date "2020-03-04" @default.
- W4287829027 modified "2023-10-16" @default.
- W4287829027 title "Learning Fast Adaptation on Cross-Accented Speech Recognition" @default.
- W4287829027 doi "https://doi.org/10.48550/arxiv.2003.01901" @default.
- W4287829027 hasPublicationYear "2020" @default.
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