OkwuGbé：Fon和Igbo的端到端语音识别
OkwuGbé: End-to-End Speech Recognition for Fon and Igbo
摘要：
语言是人类交际所固有的、必不可少的。无论是以书面还是口头方式表达，它都确保了同一地区和不同地区人民之间的理解。随着越来越多的意识和努力将更多的低资源语言纳入NLP研究，非洲语言最近成为机器翻译和其他基于文本的NLP领域的主要研究课题。然而，在非洲语言的语音识别方面的可比研究仍然很少。有趣的是，非洲语言影响NLP的一些独特属性，比如它们的变音和音调复杂性，主要源于它们的语音，这表明仔细的语音解释可以为基于文本的NLP提供更多关于如何处理非洲语言语言复杂性的直觉。OkwuGbé是为非洲低资源语言建立语音识别系统的一步。我们以Fon和Igbo为案例研究，对每种语言进行全面的语言分析，并描述了为两种语言创建端到端、基于深度神经网络的语音识别模型。我们提出了一个国家的最先进的ASR模型为Fon，以及基准ASR模型的结果为Igbo。我们的语言分析（Fon和Igbo）为其他非洲低资源语言的语音识别模型的创建提供了有价值的见解和指导，并指导Fon和Igbo未来的NLP研究。Fon和Igbo模型的源代码已经公开。
英文摘要：
Language is inherent and compulsory for human communication. Whether expressed in a written or spoken way, it ensures understanding between people of the same and different regions. With the growing awareness and effort to include more low-resourced languages in NLP research, African languages have recently been a major subject of research in machine translation, and other text-based areas of NLP. However, there is still very little comparable research in speech recognition for African languages. Interestingly, some of the unique properties of African languages affecting NLP, like their diacritical and tonal complexities, have a major root in their speech, suggesting that careful speech interpretation could provide more intuition on how to deal with the linguistic complexities of African languages for text-based NLP. OkwuGbé is a step towards building speech recognition systems for African low-resourced languages. Using Fon and Igbo as our case study, we conduct a comprehensive linguistic analysis of each language and describe the creation of end-to-end, deep neural network-based speech recognition models for both languages. We present a state-of-art ASR model for Fon, as well as benchmark ASR model results for Igbo. Our linguistic analyses (for Fon and Igbo) provide valuable insights and guidance into the creation of speech recognition models for other African low-resourced languages, as well as guide future NLP research for Fon and Igbo. The Fon and Igbo models source code have been made publicly available.
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。
