论文标题
使用语言模型改善指示语言的语音识别
Improving Speech Recognition for Indic Languages using Language Model
论文作者
论文摘要
我们研究应用语言模型（LM）对指示语言自动语音识别（ASR）系统输出的影响。我们微调WAV2VEC $ 2.0 $型号的$ 18 $指示性语言，并通过根据来自各种来源的文本训练的语言模型调整结果。我们的发现表明，平均字符错误率（CER）降低了$ 28 $ \％，平均单词错误率（WER）在用LM解码后降低了$ 36 $ \％。我们表明，与多样化的LM相比，大型LM可能无法提供实质性的改进。我们还证明，可以在特定于域的数据上获得高质量的转录，而无需重新培训ASR模型并显示了生物医学领域的结果。
We study the effect of applying a language model (LM) on the output of Automatic Speech Recognition (ASR) systems for Indic languages. We fine-tune wav2vec $2.0$ models for $18$ Indic languages and adjust the results with language models trained on text derived from a variety of sources. Our findings demonstrate that the average Character Error Rate (CER) decreases by over $28$ \% and the average Word Error Rate (WER) decreases by about $36$ \% after decoding with LM. We show that a large LM may not provide a substantial improvement as compared to a diverse one. We also demonstrate that high quality transcriptions can be obtained on domain-specific data without retraining the ASR model and show results on biomedical domain.
