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英语语音合成中基于有限泛化法的字素切分规则的机器学习
Source: webvpn.xjtu.edu.cn
Article summary:
1. This article proposes a machine learning algorithm based on Finite Generalization Algorithm (FGA) for English phoneme segmentation rules in English speech synthesis.
2. The algorithm was tested using a dictionary of 27040 words, with 90% used for rule learning and 10% used for testing.
3. After 10 rounds of cross-validation, the average instance segmentation accuracy was 99.84% and 97.88%, while the average word segmentation accuracy was 99.72% and 96.35%.
Article analysis:
The article is generally reliable and trustworthy as it provides detailed information about the proposed machine learning algorithm and its results from testing with a large dataset of 27040 words. The authors also provide clear explanations of their methodology, which makes it easy to understand how they arrived at their conclusions. Furthermore, the authors have provided references to other relevant research papers that support their claims, which adds to the trustworthiness of the article.
However, there are some potential biases that should be noted in this article. For example, the authors do not discuss any possible risks associated with using this algorithm or any potential limitations that may arise from its use in speech synthesis applications. Additionally, there is no discussion of alternative approaches or counterarguments to their proposed method, which could provide further insight into its effectiveness and reliability in real-world applications. Finally, there is no mention of any ethical considerations related to using this algorithm in speech synthesis applications such as privacy concerns or potential misuse of data generated by this algorithm.