Speaker recognition using PCA-based feature transformation

Ahmed, Ahmed Isam; Chiverton, John; Ndzi, David; Becerra, Victor M.

doi:10.1016/j.specom.2019.04.001

Cited by 18 publications

(7 citation statements)

References 48 publications

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“…Then, the extracted patterns were applied to the test banknote to classify to which denomination the respective banknote belongs [12]. Another study applied principal component analysis (PCA) [13] as a handcrafted feature-based method [14]. In this study [14], the region of interest (RoI) was extracted based on the number parts located to the left and right of the denominations.…”

Section: Related Workmentioning

confidence: 99%

Deep Feature-Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People

et al. 2020

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Owing to the rapid advancements in smartphone technology, there is an emerging need for a technology that can detect banknotes and coins to assist visually impaired people using the cameras embedded in smartphones. Previous studies have mostly used handcrafted feature-based methods, such as scaleinvariant feature transform or speeded-up robust features, which cannot produce robust detection results for banknotes or coins captured in various backgrounds and environments. With the recent advancement in deep learning technology, some studies have been conducted on banknote and coin detection using a deep convolutional neural network (CNN). However, these studies also showed degraded performance depending on the changes in background and environment. To overcome these drawbacks, this paper proposes a threestage detection technology for new banknotes and coins by applying faster region-based CNN, geometric constraints, and the residual network (ResNet). In the experiment performed using the open database of Jordanian dinar (JOD) and 6,400 images of eight types of Korean won banknotes and coins obtained using our smartphones, the proposed method exhibited a better detection performance than the state-of-the-art methods based on handcrafted features and deep features.

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Section: Related Workmentioning

confidence: 99%

Deep Feature-Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People

et al. 2020

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“…Conventional PCA, which is used in this work, may not account for potential anomalies in the data such as outliers. A future work can investigate alternative approaches to PCA in gender classification, for example, weighted PCA, see [9]. The approach taken here has the advantage of being straightforward, but the work in [9] would enable weighting the feature vectors to reduce the effect of outliers on PCA analysis.…”

Section: Discussionmentioning

confidence: 99%

“…This has motivated the investigation of the effect of using less correlated features derived from MFCC in gender-classification. A common transformation of MFCC into less correlated sets of features is through Principal Component Analysis (PCA) [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Speaker Gender Classification using Transformed Features

Ahmed¹,

Chiverton

Ndzi

et al. 2021

2021 International Conference on Communication &Amp; Information Technology (ICICT)

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Speech and image processing are fundamental components of artificial intelligence technology. Speech processing can be deployed to acquire unique features of a person's voice. These can then be used for speaker identification in addition to gender and age classification. This paper studies the effect of relative degree of correlation in speech features on gender classification. To this end, gender classification performance is evaluated using orthogonally transformed speech features. The performance is then compared to the case when speech features are used without transformation. Two machine learning approaches are used in the evaluation. One of them primarily depends on Gaussian Mixture Models (GMM) and the other one uses Support Vector Machine (SVM). The results show that less correlated speech features, obtained after the orthogonal transformation, provide better classification performance.

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“…However, it fails to work with data that has a non-linear structure. In kernel PCA, nonlinearly structured input data are converted into a higher-dimensional based feature space with a linear structure, and linear PCA is then carried out in the linearly structured high-dimensional space [7]. The PCA-based model follows the steps as stated below:…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Identifying and analysing the phonetics of low resources Nyishi language using artificial neural network technique

Ganu,

Arun

2024

Eng. Res. Express

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Phonemic identification is an analysis and classification based on sounds. Phonetics can be categorized into three various types based on how it is articulated, perceived, and produced. The study introduces new Niyshi phonetic identification approaches based on artificial neural networks to improve the detectability of the system. This work has the goal of coming up with an effective technique that can be used to detect both non-nasalized and nasalized phonemes; by classifying phonemes into those belonging or not originating from the Nyishi dialect. The model that can be used to train, test, and validate can include a multilayer-based perceptron classifier. The proposed model identifies a complex old language with an accuracy as high as 87% through Multi-Layer Perceptron (MLP). To end, a confusion matrix analysis for ten trials involving an average of 89% accuracy for 20% trained dataset validation purposes confirms the efficiency and efficacy of the methodologies employed.

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Speaker recognition using PCA-based feature transformation

Cited by 18 publications

References 48 publications

Deep Feature-Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People

Deep Feature-Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People

Machine Learning based Speaker Gender Classification using Transformed Features

Identifying and analysing the phonetics of low resources Nyishi language using artificial neural network technique

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