Abstract
This paper addresses the problem of improving speech recognition accuracy with lattice rescoring in low-resource languages where the baseline language model is insufficient for generating inclusive lattices. We minimally augment the baseline language model with word unigram counts that are present in a larger text corpus of the target language but absent in the baseline. The lattices generated after decoding with such an augmented baseline language model are more comprehensive. We obtain 21.8% (Telugu) and 41.8% (Kannada) relative word error reduction with our proposed method. This reduction in word error rate is comparable to 21.5% (Telugu) and 45.9% (Kannada) relative word error reduction obtained by decoding with full Wikipedia text augmented language mode while our approach consumes only 1/8th the memory. We demonstrate that our method is comparable with various text selection-based language model augmentation and also consistent for data sets of different sizes. Our approach is applicable for training speech recognition systems under low resource conditions where speech data and compute resources are insufficient, while there is a large text corpus that is available in the target language. Our research involves addressing the issue of out-of-vocabulary words of the baseline in general and does not focus on resolving the absence of named entities. Our proposed method is simple and yet computationally less expensive.
Abstract (translated)
本文旨在探讨在低资源语言中使用格子评分法提高语音识别准确性的问题，其中基线语言模型不足以生成包括所有词汇的包容性格子。我们通过在目标语言的大型文本语料库中存在的单词单词计数来最小化基线语言模型的扩展，这样扩展的基线语言模型可以更全面地生成格子。使用这种扩展的基线语言模型生成的格子更全面。我们提出的方法使泰米尔语（Telugu）和坎纳达语（Kannada）的相对单词错误率分别降低了21.8%和41.8%。这种减少的单词错误率与通过完整维基百科文本增强语言模式进行解码获得的相对单词错误率（分别为21.5%和45.9%）相当。我们证明了我们的方法与各种基于文本选择的语言模型增强方法相当，同时也与不同规模的数据集保持一致。我们的方法适用于在低资源条件下训练语音识别系统，其中语音数据和计算资源不足，而目标语言中存在大量文本语料库。我们的研究涉及解决基线词汇中的非词语词汇问题，并不针对解决命名实体缺失的问题。我们提出的方法简单而且计算成本较低。
URL
https://arxiv.org/abs/2403.10937
