Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

Alasadi, A. A.; Aldhayni, T. H.; Deshmukh, Ratnadeep R.; Alahmadi, Ahmed; Alshebami, Ali Saleh

doi:10.48084/etasr.3465

Cited by 12 publications

(12 citation statements)

References 36 publications

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“…The various techniques for extraction of texture and color features include color correlogram, color histogram, color cooccurrence matrix, wavelet transform and Tamura feature [6]. Several recent works have focused on the use of machine learning and Deep Learning for feature extraction [7][8][9]. The convolution layers in CNN can change the shape of the output, thereby enabling the learning of basic object shapes in the primary layers and more complex objects in the deeper layers, with a drastic reduction in the error rate [10].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Mathews¹,

Sejal²,

Venugopal³

2022

IJACSA

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Image retrieval using a textual query becomes a major challenge mainly due to human perception subjectivity and the impreciseness of image annotations. These drawbacks can be overcome by focusing on the content of images rather than on the textual descriptions of images. Traditional feature extraction techniques demand for expert knowledge to select the limited feature types and are also sensitive to changing imaging conditions. Deep feature extraction using Convolutional Neural Network (CNN) are a solution to these drawbacks as they can learn the feature representations automatically. This work carries out a detailed performance comparison of various pretrained models of CNN in feature extraction. Features are extracted from men footwear and women clothing datasets using the VGG16, VGG19, InceptionV3, Xception and ResNet50 models. Further, these extracted features are used for classification using SVM, Random Forest and K-Nearest Neighbors classifiers. Results of feature extraction and image retrieval show that VGG19, Inception and Xception features perform well with feature extraction, achieving a good image classification accuracy of 97.5%. These results are further justified by performing a comparison of image retrieval efficiency, with the extracted features and similarity metrics. This work also compares the accuracy obtained by features extracted by the selected pre-trained CNN models with the results obtained using conventional classification techniques on CIFAR 10 dataset. The features extracted using CNN can be used in image-based systems like recommender systems, where images have to be analyzed to generate item profiles.

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Section: Related Workmentioning

confidence: 99%

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Mathews¹,

Sejal²,

Venugopal³

2022

IJACSA

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“…MFCC is a very popular and efficient technique for signal processing where the frequency bands are distributed depending on the Mel-scale [15,18]. This research presents a new purpose of working with MFCC by using it to extract the features of chest X-ray images.…”

Section: ) Mel Frequency Cepstral Coefficient (Mfcc) Featuresmentioning

confidence: 99%

Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier

Owida

Al-Ghraibah

Altayeb

2021

Eng. Technol. Appl. Sci. Res.

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The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet features, where the classification using MFCC features, MFCC-delta, and MFCC-delta-delta features reached accuracy around 97% on average. The results show that the proposed model has reached the required level of success to be applicable in COVID 19 diagnosis.

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“…The recognition rate of this work reached 100%. Alasadi et al [21], different feature extraction methods have been proposed for the ASR system: the Modified Group Delay Function (ModGDF), the PNCC, and the MFCC. Forty speakers contributed 18 Arabic words to the data set.…”

Section: Introductionmentioning

confidence: 99%

Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN

Safi,

Abbas

2023

ETJ

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Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

Cited by 12 publications

References 36 publications

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier

Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN

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