论文标题
自动语音识别的端到端代码切换语言模型
End-to-End Code Switching Language Models for Automatic Speech Recognition
论文作者
论文摘要
在本文中，我们特别研究了代码开关文本，这是全球双语社区中最常见的事件之一。由于从自动语音识别（ASR）模块中提取了代码切换文本的差异，从而从代码切换的文本中提取单语文本，我们提出了一种方法，提出了一种方法，用于使用深层双向语言（LM）提取单语文本（LM）（LM）（例如Bert和其他机器翻译模型），并探索型号的模型，以及探索不同的型号。我们还通过将困惑和其他不同指标（如WER）与标准双语文本输出进行比较，解释了模型的鲁棒性。
In this paper, we particularly work on the code-switched text, one of the most common occurrences in the bilingual communities across the world. Due to the discrepancies in the extraction of code-switched text from an Automated Speech Recognition(ASR) module, and thereby extracting the monolingual text from the code-switched text, we propose an approach for extracting monolingual text using Deep Bi-directional Language Models(LM) such as BERT and other Machine Translation models, and also explore different ways of extracting code-switched text from the ASR model. We also explain the robustness of the model by comparing the results of Perplexity and other different metrics like WER, to the standard bi-lingual text output without any external information.
