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- W4386939449 abstract "Taiwan-accented speech bears similarities to the Mandarin Min dialect, but with substantial differences in vocabulary, which significantly impacts spoken language recognition outcomes. This paper concentrates on integrating pre-trained language models (PLMs) with state-of-the-art self-supervised learning (SSL)-based speech recognition systems for Taiwan-accented speech recognition tasks. We propose a progressive error correction process in tandem with recognition to fully exploit the autoregressive nature of PLM models. Experimental results demonstrate that our method effectively addresses recognition errors stemming from misspelled vocabulary in accented speech. Our proposed progressive approach achieves roughly a 0.5% improvement compared to the conventional method. Furthermore, we demonstrate that fine-tuning PLMs solely with the text from the accented dataset can enhance recognition performance, despite the limitations of accented speech resources." @default.
- W4386939449 created "2023-09-22" @default.
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- W4386939449 date "2023-01-01" @default.
- W4386939449 modified "2023-10-16" @default.
- W4386939449 title "Correction while Recognition: Combining Pretrained Language Model for Taiwan-Accented Speech Recognition" @default.
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- W4386939449 doi "https://doi.org/10.1007/978-3-031-44195-0_32" @default.
- W4386939449 hasPublicationYear "2023" @default.
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