The Effects of Data Size on Text-Independent Automatic Speaker Identification System

Mokgonyane, Tumisho Billson; Sefara, Tshephisho Joseph; Manamela, Madimetja Jonas; Modipa, Thipe I.

doi:10.1109/icabcd.2019.8851018

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Pitch frequency is a perceptual characteristic of the speech signal with physical properties denoted by F0 and is used to improve the performance of speaker identification [23,24]. Many studies use these features for speaker identification not only separately but also in combination [25][26][27][28]. In this paper, six different feature extraction approaches, namely Mel Frequency Cepstral Coefficients (MFCC)+Pitch, Gammatone Cepstral Coefficients (GTCC)+Pitch, MFCC+GTCC+Pitch+eight spectral features, spectrograms, i-vectors, and Alexnet feature vectors were used.…”

Section: Related Workmentioning

confidence: 99%

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

KESER,

GEZER

2024

Preprint

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Speaker identification is crucial in many application areas, such as automation, security, and user experience. This study examines the use of traditional classification algorithms and hybrid algorithms, as well as newly developed subspace classifiers, in the field of speaker identification. In the study, six different feature structures were tested for the various classifier algorithms. Stacked Features-Common Vector Approach (SF-CVA) and Hybrid CVA-FLDA (HCF) subspace classifiers are used for the first time in the literature for speaker identification. In addition, CVA is evaluated for the first time for speaker recognition using hybrid deep learning algorithms. This paper is also aimed at increasing accuracy rates with different hybrid algorithms. The study includes Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), i-vector + PLDA, Time Delayed Neural Network (TDNN), AutoEncoder + Softmax (AE + Softmaxx), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Common Vector Approach (CVA), SF-CVA, HCF, and Alexnet classifiers for speaker identification. The six different feature extraction approaches consist of Mel Frequency Cepstral Coefficients (MFCC) + Pitch, Gammatone Cepstral Coefficients (GTCC) + Pitch, MFCC + GTCC + Pitch + eight spectral features, spectrograms,i-vectors, and Alexnet feature vectors. For SF-CVA, 100% accuracy was achieved in most tests by combining the training and test feature vectors of the speakers separately. RNN-LSTM, i-vector + KNN, AE + softmax, TDNN, and i-vector + HCF classifiers gave the highest accuracy rates in the tests performed without combining training and test feature vectors.

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Section: Related Workmentioning

confidence: 99%

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

KESER,

GEZER

2024

Preprint

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“…E ( ω ), the excitation spectrum, corresponds to high spectral variations (details) predominantly found in high quefrency, while H ( ω ), the vocal tract, accounts for low spectral variations (envelope) present at low quefrency. Evidently, research has validated the information-rich nature of the speech spectrum envelope compared to its details [ 25 ].…”

Section: Feature Extraction Stagesmentioning

confidence: 99%

“…Within this context, MFCCs emerge as the preferred choice due to their superior alignment with the human auditory system response [ 25 ]. This alignment is achieved through the Mel-scale, which takes into consideration the frequency bands of the auditory system.…”

Section: Feature Extraction Stagesmentioning

confidence: 99%

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Hassan,

Neyazi,

Seddeq

et al. 2024

PLoS ONE

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This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.

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Domain-Specific Sentiment Analysis of Tweets Using Machine Learning Methods

Sefara,

Rangata

2023

Communications in Computer and Information Science

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The Effects of Data Size on Text-Independent Automatic Speaker Identification System

Cited by 10 publications

References 31 publications

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Domain-Specific Sentiment Analysis of Tweets Using Machine Learning Methods

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