论文标题
在土耳其ASR上的实验，并具有自我监督的语音表示
Experiments on Turkish ASR with Self-Supervised Speech Representation Learning
论文作者
论文摘要
虽然土耳其语在低资源语言中列出，但有关土耳其自动语音识别（ASR）的文献相对较古老。在本报告中，我们使用Hubert介绍了关于土耳其ASR的发现。我们通过在线资源策划的大规模数据调查了土耳其人的培训前Hubert。我们使用YouTube的6,500小时的语音数据预先培训我们的模型。结果表明，这些模型还没有准备好用于商业用途，因为它们对通常在现实世界中发生的干扰（例如重音，语，背景噪声和干扰的变化）不强大。我们分析了典型的错误和用于商业环境中使用的模型的局限性。
While the Turkish language is listed among low-resource languages, literature on Turkish automatic speech recognition (ASR) is relatively old. In this report, we present our findings on Turkish ASR with speech representation learning using HUBERT. We investigate pre-training HUBERT for Turkish with large-scale data curated from online resources. We pre-train our model using 6,500 hours of speech data from YouTube. The results show that the models are not ready for commercial use since they are not robust against disturbances that typically occur in real-world settings such as variations in accents, slang, background noise and interference. We analyze typical errors and the limitations of the models for use in commercial settings.
