基于古兰经背诵者数据集的Wav2Vec2 .0和HuBERT语音识别
Towards an Efficient Voice Identification Using Wav2Vec2.0 and HuBERT Based on the Quran Reciters Dataset
摘要：
当前的认证和可信系统依赖于经典的生物识别方法来识别或授权用户。这些方法包括音频语音识别、眼睛和手指签名。最近的工具利用深度学习和Transformers来实现更好的结果。在本文中，我们使用Wav2Vec2.0和HuBERT音频表示学习工具开发了一个用于阿拉伯语说话者识别的深度学习构造模型。端到端Wav2Vec2.0范例通过随机掩蔽一组特征向量来获取上下文化的语音表示学习，然后应用Transformer神经网络。我们采用的MLP分类器，能够区分不变的标记类。我们展示了几个实验结果，保证了所提出的模型的高精度。实验结果表明，在Wav2Vec2.0和HuBERT的情况下，对某说话人的任意波形信号的识别率分别为98%和97.1%。
英文摘要：
Current authentication and trusted systems depend on classical and biometric methods to recognize or authorize users. Such methods include audio speech recognitions, eye, and finger signatures. Recent tools utilize deep learning and transformers to achieve better results. In this paper, we develop a deep learning constructed model for Arabic speakers identification by using Wav2Vec2.0 and HuBERT audio representation learning tools. The end-to-end Wav2Vec2.0 paradigm acquires contextualized speech representations learnings by randomly masking a set of feature vectors, and then applies a transformer neural network. We employ an MLP classifier that is able to differentiate between invariant labeled classes. We show several experimental results that safeguard the high accuracy of the proposed model. The experiments ensure that an arbitrary wave signal for a certain speaker can be identified with 98% and 97.1% accuracies in the cases of Wav2Vec2.0 and HuBERT, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
