Forsake 发表于 2025-3-25 05:53:30
Noise-Robust Gender Classification System Through Optimal Selection of Acoustic Features,resence of loud backgrounds as well as their evaluations and possible impacts on practical efficiency. Finally, three semi-supervised classification algorithms including random forest, support vector machine (SVM), and multi-layer perceptron (MLP) have been experimented resulting in the increased peBROTH 发表于 2025-3-25 09:26:06
https://doi.org/10.1007/978-3-642-70926-5have become interested in analyzing people’s feelings through social networks, especially in political and economic domains. The main task is to classify the level of messages or tweets to their polarity. In this research, we will look at the most important approaches used in sentiment analysis andEndemic 发表于 2025-3-25 14:07:12
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Junjie Li,Shuo Tian,Yuanhui LiuC) have been recorded with modest changes using hidden Markov models (HMM). The selection of optimal features was made possible by increasing child data through adaptation measures on adult data, which has allowed for the examination of new features under mismatched conditions resulting in an overal易于 发表于 2025-3-25 20:02:43
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Design and Testing of Reversible Logicion technique. Analysis of this chapter indicates that classical feature extraction techniques of cepstral domain like Mel Frequency Cepstral Coefficients (MFCC) are the most popular and better in performance for speech and speaker recognition tasks. This chapter provides the implementation detailslegitimate 发表于 2025-3-26 08:04:14
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Book 2021 techniques can be applied to improve NLP and speech processing applications;.Presents and escalates the research trends and future direction of language and speech processing;.Includes theoretical research, experimental results, and applications of deep learning..anchor 发表于 2025-3-26 19:46:23
1860-4862escalates the research trends and future direction of language and speech processing;.Includes theoretical research, experimental results, and applications of deep learning..978-3-030-79780-5978-3-030-79778-2Series ISSN 1860-4862 Series E-ISSN 1860-4870