计算机科学
语音识别
词汇
人工智能
语言模型
自然语言处理
字错误率
利用
口语
过程(计算)
隐马尔可夫模型
语言学
哲学
计算机安全
操作系统
标识
DOI:10.1007/978-3-031-44195-0_32
摘要
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.
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