GMM based automatic speaker verification system development for forensics in Bahasa Indonesia

Stefanus, Ivan; Sarwono, Joko; Mandasari, Miranti Indar

doi:10.1109/ica.2017.8068413

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…According to the literature review paper done by [2] GMM based AVR is the best method because it is excellent for voice pattern recognition problem. In addition [27] developed a GMM based automatic voice verification system development for forensics in Bahasa Indonesia. Furthermore [28,29] have proved from their research work that GMM is the best for voice recognition, then followed by DTW and finally the less performing algorithm, namely SVM.…”

Section: Related Work Of Voice Recognitionmentioning

confidence: 99%

A Novel automatic voice recognition system based on text-independent in a noisy environment

Hamza¹,

Khodadadi²,

Palaniappan³

2020

IJECE

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Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user to speak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. The voice recognition was done in two steps: training and testing. During the training a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisy environment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noise methods were applied, we investigated different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that subsequently improved the performance of the automatic voice recognition system.

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Section: Related Work Of Voice Recognitionmentioning

confidence: 99%

A Novel automatic voice recognition system based on text-independent in a noisy environment

Hamza¹,

Khodadadi²,

Palaniappan³

2020

IJECE

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“…Selama ini, kebanyakan sistem rekognisi pengucap forensik yang dibangun dan digunakan di Indonesia berbasiskan metode semi-otomatis dengan fitur fonetik-akustik [8]. Paradigma ini telah berubah dengan pengembangan sistem rekognisi pengucap otomatis dengan berbagai metode yang ada [9,10], mulai dari teknik Gaussian Mixture Model -Universal Background Model (GMM-UBM) [11], klasifikasi dengan Support Vector Machine (SVM) [12], pemodelan i-vector [13,14], teknik perhitungan skor dengan Probabilistic Linear Discriminant Analysis (PLDA) [15,16], sampai dengan teknik deeplearning yang berbasis jaringan syaraf tiruan [17,18].…”

Section: Pendahuluanunclassified

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Mandasari¹,

Firmanto²,

Fathurrahman³

2019

JLK

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Likelihood ratio calibration is a very important step when applying an automatic speaker recognition system to forensic applications. This paper presents the process and evaluation of a speaker recognition system developed with spoken Indonesian database. The system is developed using MFCC feature, GMM-UBM modeling, and Z normalization. System performances were evaluated based on male and female genders, and two scenarios i.e., natural conversation and interview. System evaluations were done using performance measures for both discrimination and calibration abilities. Results show that based on various indicators, the system behaves very well. This is shown by the best achievable EER values of 4.66%, and Cmc values of 0.04. Therefore, the developed automatic speaker recognition system is now ready to be used for forensic applications in Indonesia.

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“…In forensic cases it is examined whether a questioned voice belongs to a suspect or not [18]. With accent recognition it becomes relatively simple because the speaking style and way of pronunciation vary from person to person and region to region and results in distinct speech features, which can be used for accent recognition [19].…”

Section: Related Workmentioning

confidence: 99%

Extracting accent information from Urdu speech for forensic speaker recognition

Tahir¹,

Saleem²,

Ahmad³

2019

Turk J Elec Eng & Comp Sci

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This paper presents a new method for extraction of accent information from Urdu speech signals. Accent is used in speaker recognition system especially in forensic cases and plays a vital role in discriminating people of different groups, communities and origins due to their different speaking styles. The proposed method is based on Gaussian mixture model-universal background model (GMM-UBM), mel-frequency cepstral coefficients (MFCC), and a data augmentation (DA) process. The DA process appends features to base MFCC features and improves the accent extraction and forensic speaker recognition performances of GMM-UBM. Experiments are performed on an Urdu forensic speaker corpus. The experimental results show that the proposed method improves the equal error rate and the accuracy of GMM-UBM by 2.5% and 3.7%, respectively.

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GMM based automatic speaker verification system development for forensics in Bahasa Indonesia

Cited by 10 publications

References 8 publications

A Novel automatic voice recognition system based on text-independent in a noisy environment

A Novel automatic voice recognition system based on text-independent in a noisy environment

Untitled

Extracting accent information from Urdu speech for forensic speaker recognition

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