Classification of Bangla Alphabets Phoneme based on Audio Features using MLPC &amp; SVM

Hussain, Gulzar; Rahman, Mohammad Lutfur; Sultana, Babe; Khatun, Ayesha; Hasan, Sakib Al

doi:10.1109/acmi53878.2021.9528088

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Spectral periodicity decides if in the LS periodogram is a significant peak that indicates the periodicity [42]. Spectral rolloff is defined as frequency bellow at is concentrated 85% of the distribution power [43]. Spectral spread is the difference between the highest and lowest frequency in the power spectrum [44].…”

Section: Spectral Kurtosis Can Indicate a Non-stationary Or Non-mentioning

confidence: 99%

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Network traffic classification based on periodic behavior detection

Koumar

Čejka

2022

2022 18th International Conference on Network and Service Management (CNSM)

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Network traffic monitoring using IP flows is used to handle the current challenge of analyzing encrypted network communication. Nevertheless, the packet aggregation into flow records naturally causes information loss; therefore, this paper proposes a novel flow extension for traffic features based on the time series analysis of the Single Flow Time series, i.e., a time series created by the number of bytes in each packet and its timestamp. We propose 69 universal features based on the statistical analysis of data points, time domain analysis, packet distribution within the flow timespan, time series behavior, and frequency domain analysis. We have demonstrated the usability and universality of the proposed feature vector for various network traffic classification tasks using 15 well-known publicly available datasets. Our evaluation shows that the novel feature vector achieves classification performance similar or better than related works on both binary and multiclass classification tasks. In more than half of the evaluated tasks, the classification performance increased by up to 5 %.

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Section: Spectral Kurtosis Can Indicate a Non-stationary Or Non-mentioning

confidence: 99%

“…Spectral slope is the slope of the power spectrum trend in a given frequency range [31]. Spectral zero crossing rate refers to the rate of power shifts, i.e., the change from negative to positive or the reverse [43].…”

Section: Spectral Kurtosis Can Indicate a Non-stationary Or Non-mentioning

confidence: 99%

Network traffic classification based on periodic behavior detection

Koumar

Čejka

2022

2022 18th International Conference on Network and Service Management (CNSM)

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“…The literature [11] used Gaussian mixture model (GMM) and linear discriminant analysis (LDA) for voice recognition. The literature [12] employed SVM with multilayer perceptron classifier for phoneme classification of Bengali. Automatic speech recognition (ASR) based pronunciation error detection and ASR have similarity in the construction of acoustic models.…”

Section: Introductionmentioning

confidence: 99%

An English Pronunciation Error Detection System Based on Improved Random Forest

Cao

Dong

2022

Mobile Information Systems

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The existing English pronunciation error detection methods are more oriented to the detection of wrong pronunciation, and lack of targeted improvement suggestions for pronunciation errors. With the aim of solving this problem, the paper proposes an English pronunciation error detection system based on improved random forest. Firstly, a speech corpus is constructed along with the evaluation of the acoustic features. Then an improved random forest detection algorithm is designed. The algorithm inputs rare mispronunciation data into a GAN neural network to generate new class samples and improve the uneven distribution of mispronunciation data in the sample set. The distribution rules of the pronunciation data are extracted layer by layer by stacking deep SDAEs, and the coefficient penalties and reconstruction errors of each coding layer are combined to identify the features associated with the wrong pronunciation in the high-dimensional data. Furthermore, a forest decision tree is constructed using the reduced-dimensional feature-based data to improve the pronunciation detection accuracy. Finally, the extracted 39 Mel Frequency Cepstral Coefficient (MFCC) acoustic features are used as the input of the improved random forest classifier to construct a classification error detection model. The experimental results indicate that the designed system achieves a high accuracy of English pronunciation detection.

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Classification of Bangla Alphabets Phoneme based on Audio Features using MLPC & SVM

Cited by 2 publications

References 13 publications

Network traffic classification based on periodic behavior detection

Network traffic classification based on periodic behavior detection

An English Pronunciation Error Detection System Based on Improved Random Forest

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