Advanced feature extraction method for speaker identification using a classification algorithm

Identifying a person's identity still often uses an ID card (KTP, SIM, passport, etc.). This method has a weakness because the ID Card is easily damaged and lost. Biometric recognition systems provide a solution by using human body parts as identity recognition. Sounds are readily available biometric information. Voice pattern recognition is used for the speaker identification process to obtain the identity of someone speaking. This paper reviews several feature extraction and classification methods that are often used in speaker identification. The selection of feature extraction methods and classification functions in computation and the level of accuracy of the speaker identification system. Based on the survey dataset applied with the feature extraction method, the Mel Frequency Cepstral Coefficients (MFCC) method has high accuracy even with noise input. Then in classification, the Gaussian Mixture Model (GMM) method is most often used because it can work in noise. Recently, a hybrid classifier has been developed, which increases the accuracy value.

Gaussian Mixture models is one of the most popular statistical methods in speaker recognition. The purpose of this research is to perform experiments of speaker recognition using various feature vectors: four formants, four formants with fundamental frequency and mel cepstrum coefficients. Gaussian mixture models using mel cepstrum coefficients is baseline in speaker recognition and gives one of the best results in text independent speaker recognition. After implementing experiments of speaker recognition and comparing experimental results we can affirm that mel scale cepstral coefficients and four formants with fundamental frequency gives quite the same recognition accuracy, but creating of Gaussian mixture speaker models and recognition process continues a few times longer using mel scale cepstral coefficients, because count of calculations is few times greater in that case. Using only four formants gives the worst results of recognition accuracy. Ill. 7, bibl. 12 (in English; summaries in English, Russian and Lithuanian).

The Mel-Frequency Cepstral Coefficients (MFCC) feature extraction method is a leading approach for speech feature extraction and current research aims to identify performance enhancements. One of the recent MFCC implementations is the Delta-Delta MFCC, which improves speaker verification. In this paper, a new MFCC feature extraction method based on distributed Discrete Cosine Transform (DCT-II) is presented. Speaker verification tests are proposed based on three different feature extraction methods including: conventional MFCC, Delta-Delta MFCC and distributed DCT-II based Delta-Delta MFCC with a Gaussian Mixture Model (GMM) classifier.

In this work, fast and efficient appearance-based methods for visual loop-closure detection are proposed. The widely used technique based on the Bag-of-Words image representation has shown some limitations especially with aliasing problem. In this work, however, an appearance-based approach for loop closure detection using local invariant and colour features is proposed. The first technique uses Bayes Decision Theory for loop closure detection based on Gaussian Mixture Model (GMM). A new technique based on the combination of GMM with the KD-Tree data structure is presented as well. The techniques have been validated using monocular image sequences from several environments.

In this work we present a vision-based road user monitoring system for traffic intersections using a combination of Gaussian Mixture Model (GMM)-based deep learning approaches and geometric warping for further behaviour analysis. GMMs and detection of features in consecutive frames are used in bounding box prediction in order to track objects, while a Fast Regional Convolutional Neural Networks (R-CNN) approach classifies different road users. For better analysis, by knowing the actual coordinates of the intersection, we use geometric warping to map the 3D-plane to the 2D plane. We thus extract the real distance and the approximate real time speed of each road user. The results demonstrate that the proposed region proposal generator method for Fast R-CNN outperforms the other methods considered in terms of both accuracy and computation time.

BackgroundAlzheimer's disease (AD) is the most common type of dementia, and early screening is crucial for intervention.ObjectiveCurrently, early screening for older adults without dementia primarily rely on cognitive scale. This study aims to explore a more feasible approach.MethodsPlasma biomarkers (Aβ42/40, p-tau181 and p-tau217) and Gaussian mixture models (GMM) were utilized for stratifying risk levels in older adults without dementia from the Alzheimer's Disease Neuroimaging Initiative. Linear mixed effects model was employed to compare subsequent pathological and cognitive changes, alongside a comparison with traditional scale-based screening methods. Cox regression model was used to assess the risk of progression to dementia across different biomarker status groups.ResultsPlasma Aβ42/40 and p-tau217 effectively predicted Aβ PET pathological progression, while p-tau217 also predicted tau PET changes. All three biomarkers could forecast the progression of FDG PET and cognitive function. P-tau217 and p-tau181 significantly modulated pathology-related cognitive impairment. All three biomarkers could predict dementia risk. The screening method combining GMM with plasma biomarkers demonstrates superior predictive ability compared to traditional scale-based approaches.ConclusionsOur study indicated that the combination of GMM and plasma biomarkers for community screening shows promising potential in monitoring brain health among older adults without dementia. P-tau217 exhibited the best predictive value among the three plasma biomarkers.

In the biometrics community, face and speaker recognition are mature fields in which several systems have been proposed over the past twenty years. While existing systems perform well under controlled recording conditions, mismatch caused by the use of different sensors or a lack of cooperation from the subject still significantly affects performance, especially in challenging scenarios such as in forensics. Furthermore, existing methods suffer from scalability issues, which prevents them from taking advantage of increasingly large amounts of training data. This is otherwise a promising approach to improve accuracy in such challenging scenarios. In this thesis we address these problems of mismatch and complexity by developing scalable probabilistic models that we apply to face, speaker and bimodal recognition. Our contributions are four-fold. First, we propose a unified framework for session variability modeling techniques based on Gaussian mixture models (GMM), that encompasses inter-session variability (ISV) modeling, joint factor analysis (JFA) and total variability (TV) modeling. Second, we propose a novel exact and scalable formulation of probabilistic linear discriminant analysis (PLDA), which is a probabilistic and generative framework that models between-class and within-class variations. This formulation solves a major scalability issue, by improving both the time complexity of the training procedure from cubic to linear with respect to the number of samples per class, and the complexity of the scoring procedure. Furthermore, the implementations of all the proposed techniques are integrated into a novel collaborative open source software library called Bob 1 that enforces fair evaluations and encourages reproducible research. Fourth and finally, large-scale experiments are conducted with all of the above machine learning algorithms on several databases such as FRGC for face recognition, NIST SRE12 for speaker recognition and MOBIO for bimodal recognition, showing competitive performances.

Relevance factor of maximum a posteriori adaptation for GMM–NAP–SVM in speaker and language recognition

Speech applications, which operate a system by voice commands, facilitate web access for disabled and visually impaired users. Human-computer interactions, such as speaking and listening to web applications, provide options for developing a multimodal interaction tool in the accessible design of an intelligent web. Speaker identification and verification are essential functionalities for intelligent web programs with speech applications. This paper proposes an enhanced Gaussian mixture model (GMM) method by incorporating the information derived from the support vector machine (SVM), called EGMM-SVM, for web-based applications with speaker recognition. The EGMM-SVM improves the accuracy of the estimated likelihood scores between the speech frame and the GMM. In EGMM-SVM, SVM plays a crucial role in transmitting the quality information of the utterances from a test speaker, through the GMM when performing GMM likelihood calculations. The experimental results show that speaker recognition by using the developed EGMM-SVM with an accurate operation mechanism for Gaussian distribution derivations yields a higher recognition rate than does a conventional GMM without any considerations on the quality of test speech utterances.

Residual networks for text-independent speaker identification: Unleashing the power of residual learning

Efficient similarity search for uncertain data is a challenging task in many modern data mining applications like image retrieval, speaker recognition and stock market analysis. A common way to model the uncertainty of data objects is using probability density functions in the form of Gaussian Mixture Models (GMMs), which have an ability to approximate arbitrary distribution. However, due to the possible unequal length of mixture models, the use of existing index techniques has serious problems for the objects modeled by GMMs. Either the techniques cannot handle GMMs or they have too many limitations. Hence, we propose a dynamic index structure, Gaussian Component based Index (GCI), for GMMs. GCI decomposes GMMs into the single, pairs, or n-lets of Gaussian components, stores these components into well studied index trees such as U-tree and Gauss-Tree, and refines the corresponding GMMs in a conservative but tight way. GCI supports both k-most-likely queries and probability threshold queries by means of Matching Probability. Extensive experimental evaluations of GCI demonstrate a considerable speed-up of similarity search on both synthetic and real-world data sets.

A hybrid feature selection scheme and self-organizing map model for machine health assessment

The Gaussian mixture models (GMM) represent an efficient model that was broadly used in most of speaker recognition applications. This study introduces a novel method for speaker verification task. We propose a reduced feature vector employing new information detected from the speaker's voice for performing text-independent speaker verification applications using GMM. We use the power spectrum density of the speech signal to improve the system's performance. Speaker verification experiments were evaluated with the TIMIT dataset. The suggested system performance is evaluated against the baseline systems. The decrease in the error rate is well observed and the results have demonstrated the effectiveness of the new approach which avoids the use of more complex algorithms or the combination of different approaches.

Even though speaker recognition has gained significant progress in recent years, its performance is known to be deteriorated severely with the existence of strong background noises. Inspired by a recently proposed clean-frame selection approach, this work investigates a relatively elegant weighting method when computing the Baum-Welch statistics of Gaussian mixture models (GMMs) in i-vector extraction. By introducing weighting parameters to the frames of enrollment/testing utterances, the optimization problem is redefined and solved. New updating rules are derived by incorporating weights to the computation of posterior probabilities, mean vectors, and covariance matrices of the GMM. The experiments conducted on the Speakers in the Wild (SITW) database show that the proposed algorithm has significantly improved the performance of i-vector-based speaker recognition systems in noisy environments. Compared with the GMM i-vector baseline, the equal error rate is reduced from 5.75 to 4.72 and the minimum value of cost function (C det min ) is reduced from 0.4825 to 0.4505. Slight but significant superiority is also observed over the method with an additional feature enhancement frontend by using deep neural networks.

This article investigates the development and evaluation of a speaker identification system using deep learning techniques, with a focus on Convolutional Neural Networks (CNNs) and the audioMNIST dataset. The study reveals significant advancements in speaker identification, demonstrating substantial improvements over state-of-the-art models. Our system achieves high accuracy and reliability in distinguishing speakers, showcasing its potential applications in forensic science, security, and privacy protection. The paper thoroughly examines audio signal representation, preprocessing techniques, and feature extraction methods, highlighting how these components contribute to the system's effectiveness. By leveraging CNNs, the proposed system provides highly accurate speaker identification and exhibits robustness in various conditions, including noise and varying speech patterns. The findings underscore the system’s capability to enhance security measures and forensic research, paving the way for future optimizations and broader applications. This contribution expands the knowledge base in speaker identification technology, offering scalable and efficient solutions for real-world scenarios. Future research directions include refining the dataset, exploring advanced optimization techniques, and addressing ethical considerations to ensure the system's robustness and practical utility in diverse applications.