Speaker ethnic identification for continuous speech in malay language using pitch and MFCC

Hanifa, Rafizah Mohd; Isa, Khairunesa; Mohamad, Shamsul

doi:10.11591/ijeecs.v19.i1.pp207-214

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Accuracy is the ratio of the number of cases that were predicted with correct answers compared to the total number of cases, while recall is the ratio of the number of positive cases that were predicted correctly to the number of positive cases that were predicted. Accuracy, precision, recall and F1-score measure can be calculated using the (6) to (9), respectively. Where TP, TN, FP, and FN stand for true positive, true negative, false positive, and false negative, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…In several studies regarding speaker recognition multi ethnic, the problem of limited data is an initial challenge in conducting research such as the study conducted by Hanifa et al [9], which only had 62 recorded data of Malaysian speakers, and the study of Cole [10] to identify speakers in South East England with 227 speakers. To solve the problem of limited data, various approaches are used, among others, by using ata augmentation (DA).…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

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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show abstract

“…This accuracy is higher than the traditional GMM. Another approach was employed in a study ( Hanifa, Isa & Mohamad, 2020 ) that used 52 recordings of Malaysian recorded samples utilizing the MFCC in the feature extraction, with an accuracy of 57%. Along with machine learning, numerous works in SER and multi-racial utilize the DL technique, regarded as a rigorous approach to SER.…”

Section: Related Workmentioning

confidence: 99%

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Amjad

Khan

Chang

2022

PeerJ Computer Science

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Speech emotion recognition (SER) systems have evolved into an important method for recognizing a person in several applications, including e-commerce, everyday interactions, law enforcement, and forensics. The SER system’s efficiency depends on the length of the audio samples used for testing and training. However, the different suggested models successfully obtained relatively high accuracy in this study. Moreover, the degree of SER efficiency is not yet optimum due to the limited database, resulting in overfitting and skewing samples. Therefore, the proposed approach presents a data augmentation method that shifts the pitch, uses multiple window sizes, stretches the time, and adds white noise to the original audio. In addition, a deep model is further evaluated to generate a new paradigm for SER. The data augmentation approach increased the limited amount of data from the Pakistani racial speaker speech dataset in the proposed system. The seven-layer framework was employed to provide the most optimal performance in terms of accuracy compared to other multilayer approaches. The seven-layer method is used in existing works to achieve a very high level of accuracy. The suggested system achieved 97.32% accuracy with a 0.032% loss in the 75%:25% splitting ratio. In addition, more than 500 augmentation data samples were added. Therefore, the proposed approach results show that deep neural networks with data augmentation can enhance the SER performance on the Pakistani racial speech dataset.

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“…It is a part of biometrics that might be utilized for distinguishing proof, check, and recognition of individual speakers, with the capacity of detection, tracking, and segmentation by Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  Arabic speaker recognition using HMM (Jabbar S. Hussein) 1213 extension. Speaker recognition and speaker check structure a bigger control of speaker classification [13]. Speaker recognition attempts to figure out which speaker produced a discourse signal though speaker check affirms if the part of the discourse has a place with the person who allegation it.…”

Section: Introductionmentioning

confidence: 99%

Arabic speaker recognition using HMM

Hussein

Salman²,

Saeed³

2021

IJEECS

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In this paper, a new suggested system for speaker recognition by using hidden markov model (HHM) algorithm. Many researches have been written in this subject, especially by HMM. Arabic language is one of the difficult languages and the work with it is very little, also, the work has been done for text dependent system where HMM is very effective and the algorithm trained at the word level. One the problems in such systems is the noise, so we take it in consideration by adding additive white gaussian noise (AWGN) to the speech signals to see its effect. Here, we used HMM with new algorithm with one state, where two of these components, i.e. (π and A) are removed. This give extremely accelerates the training and testing stages of recognition speeds with lowest memory usage, as seen in the work. The results show an excellent outcome. 100% recognition rate for the tested data, about 91.6% recognition rate with AWGN noise.

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Speaker ethnic identification for continuous speech in malay language using pitch and MFCC

Cited by 4 publications

References 13 publications

Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation

Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Arabic speaker recognition using HMM

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