Arabic speaker recognition using HMM

Hussein, Jabbar S.; Salman, Abdulkadhim A.; Saeed, Thmer R.

doi:10.11591/ijeecs.v23.i2.pp1212-1218

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Initially, research in the field of speaker recognition used classical methods in machine learning, gaussian mixture model (GMM), such as studies carried out by Motlicek et al [1] and Veena and Mathew [2]. In speaker recognition, the hidden markov model (HMM) strategy is also utilized, as demonstrated by Maghsoodi et al [3], Hussein, et al [4] and the support vector machine (SVM) approach on research that has been done by Chaunan et al [5].…”

Section: Introductionmentioning

confidence: 99%

Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation

Nugroho

Setiadi

et al. 2023

IJ-AI

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Speaker recognition to recognize multiethnic speakers is an interesting research topic. Various studies involving many ethnicities require the right approach to achieve optimal model performance. The deep learning approach has been used in speaker recognition research involving many classes to achieve high accuracy results with promising results. However, multi-class and imbalanced datasets are still obstacles encountered in various studies using the deep learning method which cause overfitting and decreased accuracy. Data augmentation is an approach model used in overcoming the problem of small amounts of data and multiclass problems. This approach can improve the quality of research data according to the method applied. This study proposes a data augmentation method using pitch shifting with a deep neural network called pitch shifting data augmentation deep neural network (PSDA-DNN) to identify multiethnic Indonesian speakers. The results of the research that has been done prove that the PSDA-DNN approach is the best method in multi-ethnic speaker recognition where the accuracy reaches 99.27% and the precision, recall, F1 score is 97.60%.

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Section: Introductionmentioning

confidence: 99%

Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation

Nugroho

Setiadi

et al. 2023

IJ-AI

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“…Prior to the existing situation, no one can predict what would happen in the future. An authentication system that uses biometrics must compare a previously registered biometric sample with a newly acquired biometric sample [4], [5]. A biometric sample is collected, analyzed, and saved for comparison later on during registration.…”

Section: Introductionmentioning

confidence: 99%

Enhancement and modification of automatic speaker verification by utilizing hidden Markov model

Kadhim

Nasret²,

Mahmood³

2022

IJEECS

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<div class="WordSection1"><p>The purpose of this study is to discuss the design and implementation of autonomous surface vehicle (ASV) systems. There’s a lot riding on the advancement and improvement of ASV applications, especially given the benefits they provide over other biometric approaches. Modern speaker recognition systems rely on statistical models like hidden Markov model (HMM), support vector machine (SVM), artificial neural networks (ANN), generalized method of moments (GMM), and combined models to identify speakers. Using a French dataset, this study investigates the effectiveness of prompted te xt speaker verification. At a context-free, single mixed mono phony level, this study has been constructing a continuous speech system based on HMM. After that, suitable voice data is used to build the client and world models. In order to verify speakers, the text-dependent speaker ver-ification system uses sentence HMM that have been concatenated for the key text. Normalized log-likelihood is determined from client model forced by Viterbi algorithm and world model, in the verification step as the difference between the log-likelihood. At long last, a method for figuring out the verification results is revealed.</p></div>

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Speech Recognition Based on Statistical Features

Hussein¹

2023

Computational Semantics

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The requisition of intelligent devices that might classify a vocalized utterance have been skippering utterance research. The challenging task with utterance recognition models given for the language nature whereby there’re nope apparent limits among words, an acoustic start with ending are impacted through the neighboring words, also, with various talkers utterance: female/male, senior/young, low/loud utterance, read/spontaneous, fast/slow vocalizing proportion and the utterance sign could be influenced by ambient noise. Accordingly, utterance recognition was exceeding abound of such challenges. To avert particular problems, information steered statistical curriculum built on considerable amounts of vocalized data has been utilized. With this itemize, the aim is to reconnoiter creativity that has making these implements plausible. Utterance recognition and language comprehension have been two important reconnoitering antes thereupon has normally been logged nearer as matters with indicatively and audio vocal, whereby the domain for audio vocal data have stayed introduced as robust impact to the matter thru drib accomplishment. Hence, we amid about determinate methods to utterances and language manipulating, whereby a data around a talking sign and a language that it converses, adjoining thru valuable utilized of information, is established come from inherent recognition of utterance data thru an understandable math-statistical formality.

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Arabic speaker recognition using HMM

Cited by 3 publications

References 20 publications

Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation

Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation

Enhancement and modification of automatic speaker verification by utilizing hidden Markov model

Speech Recognition Based on Statistical Features

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