This article discusses the use of SVM and GMM methods to build voice command models to solve the problem of voice command recognition. Voice recognition technology is available in a wide range of applications, including voice assistants, control systems and more. The article presents an approach based on the use of SVM and GMM methods for modeling and classifying voice commands. The article presents experiments on a dataset of Karakalpak voice commands. A comparative analysis of the performance of SVM and GMM was conducted in terms of recognition accuracy and overall performance based on feature set extraction algorithms. Experimental results show that SVM and GMM methods can achieve high accuracy in recognizing voice commands. Therefore, this paper presents a comparison between SVM and GMM methods for voice command recognition.
| Mualliflar | Nurimov, P.B. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2023-12-26 |
| Jild | 6 |
| Son | 4 |
| Betlar | 47-52 |
| Til | Rus |
| DOI | 10.62132/ijdt.v6i4.133 |
DOI: 10.62132/ijdt.v6i4.133 · Maqolaning asl sahifasi
voice signal, recognition voice commands, feature extraction, Support vector machines, Gaussian mixture model, ovoz signali, ovozli buyruqlarni tanib olish, belgilar toʻplamini ajratish, tayanch vektorlar mashinalari, Gaus aralashma modeli
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