Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Osadchyy, Volodymyr; Skuratovskii, Ruslan

doi:10.46300/9106.2020.14.125

Cited by 3 publications

(1 citation statement)

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“…The exterior loop keeps track of the tree count, while the inside loop calculates the features. Loops can be swapped out, and this action improves run time performance [27]. Parallel threads are used to scan, initialize, and sort all the instances, globally.…”

Section: Extreme Gradient Boosting Classifiermentioning

confidence: 99%

Machine learning for Arabic phonemes recognition using electrolarynx speech

Ameen

Kadhim²

2023

IJECE

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<p><span lang="EN-US">Automatic speech recognition system is one of the essential ways of interaction with machines. Interests in speech based intelligent systems have grown in the past few decades. Therefore, there is a need to develop more efficient methods for human speech recognition to ensure the reliability of communication between individuals and machines. This paper is concerned with Arabic phoneme recognition of electrolarynx device. Electrolarynx is a device used by cancer patients having vocal laryngeal cords removed. Speech recognition here is considered to find the preferred machine learning model that can classify phonemes produced by electrolarynx device. The phonemes recognition employs different machine learning schemes, including convolutional neural network, recurrent neural network, artificial neural network (ANN), random forest, extreme gradient boosting (XGBoost), and long short-term memory. Modern standard Arabic is utilized for testing and training phases of the recognition system. The dataset covers both an ordinary speech and electrolarynx device speech recorded by the same person. Mel frequency cepstral coefficients are considered as speech features. The results show that the ANN machine learning method outperformed other methods with an accuracy rate of 75%, a precision value of 77%, and a phoneme error rate (PER) of 21.85%.</span></p>

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Section: Extreme Gradient Boosting Classifiermentioning

confidence: 99%

Machine learning for Arabic phonemes recognition using electrolarynx speech

Ameen

Kadhim²

2023

IJECE

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Background Speech Synchronous Recognition Method of E-commerce Platform Based on Hidden Markov Model

Pei-hua

Wang

2022

International Journal of Circuits, Systems and Signal Processing

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In order to improve the effect of e-commerce platform background speech synchronous recognition and solve the problem that traditional methods are vulnerable to sudden noise, resulting in poor recognition effect, this paper proposes a background speech synchronous recognition method based on Hidden Markov model. Combined with the principle of speech recognition, the speech feature is collected. Hidden Markov model is used to input and recognize high fidelity speech filter to ensure the effectiveness of signal processing results. Through the de-noising of e-commerce platform background voice, and the language signal cache and storage recognition, using vector graph buffer audio, through the Ethernet interface transplant related speech recognition sequence, thus realizing background speech synchronization, so as to realize the language recognition, improve the recognition accuracy. Finally, the experimental results show that the background speech synchronous recognition method based on Hidden Markov model is better than the traditional methods.

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Research on the Improvement of Big Data Feature Investment Analysis Algorithm for Abnormal Trading in the Financial Securities Market

Zou

Gong

Huang

et al. 2022

International Journal of Circuits, Systems and Signal Processing

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Traditional investment analysis algorithms usually only analyze the similarity between financial time series and financial data, which leads to inaccurate and inefficient analysis of investment characteristics. In addition, the trading volume of financial securities market is huge, the amount of investment data is also very large, and the detection of abnormal transactions is difficult. The aim of feature extraction is to obtain mathematical features that can be recognized by machine. Different from the traditional methods, this paper studies and improves the big data investment analysis algorithm of abnormal transactions in financial securities market. After processing the captured trading data of financial securities market, the big data feature of abnormal trading is extracted. Combined with the abnormal trading and the financial securities market, the investment strategy is determined. The optimization objective function is set and the genetic algorithm is used to improve the investment analysis algorithm. The simulation experiment verifies the improved investment analysis algorithm, and the average Accuracy of investment analysis is increased by at least 11.24%, the ROI is significantly improved, and the efficiency is higher, which indicates that the proposed algorithm has ideal application performance.

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Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Cited by 3 publications

References 50 publications

Machine learning for Arabic phonemes recognition using electrolarynx speech

Machine learning for Arabic phonemes recognition using electrolarynx speech

Background Speech Synchronous Recognition Method of E-commerce Platform Based on Hidden Markov Model

Research on the Improvement of Big Data Feature Investment Analysis Algorithm for Abnormal Trading in the Financial Securities Market

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