Evaluation of Speaker Identification System using GSMEFR speech Data

Krobba, Ahmed; Debyeche, Mohamed; Amrouche, Abederrahmane

doi:10.1109/dtis.2010.5487589

Cited by 19 publications

(4 citation statements)

References 7 publications

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“…There are very few research studies that focus on the spoken Arabic language as speech [27,28]. This is mainly because Arabic speech databases are quite limited with reference to the speaker recognition area.…”

Section: Related Workmentioning

confidence: 99%

A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments

et al. 2022

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Speaker identification systems perform almost ideally in neutral talking environments. However, these systems perform poorly in stressful talking environments. In this paper, we present an effective approach for enhancing the performance of speaker identification in stressful talking environments based on a novel radial basis function neural network-convolutional neural network (RBFNN-CNN) model. In this research, we applied our approach to two distinct speech databases: a local Arabic Emirati-accent dataset and a global English Speech Under Simulated and Actual Stress (SUSAS) corpus. To the best of our knowledge, this is the first work that addresses the use of an RBFNN-CNN model in speaker identification under stressful talking environments. Our speech identification models select the finest speech signal representation through the use of Mel-frequency cepstral coefficients (MFCCs) as a feature extraction method. A comparison among traditional classifiers such as support vector machine (SVM), multilayer perceptron (MLP), k-nearest neighbors algorithm (KNN) and deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), was conducted. The results of our experiments show that speaker identification performance in stressful environments based on the RBFNN-CNN model is higher than that with the classical and deep machine learning models.

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Section: Related Workmentioning

confidence: 99%

A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments

et al. 2022

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“…The "ARADIGIT database" is made up of 60 speakers generating the ten Arabic digits with a replicate of three times each. Various experiments were accomplished to calculate the deterioration caused by diverse aspects of the simulated codec [7]. Mahmood et.al [8] proposed and implemented novel features called "Multi-Directional Local Feature (MDLF)" for speaker recognition.…”

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confidence: 99%

“…In the areas of speaker recognition and speech processing and recognition, most of the research work has been focused on speech spoken in English language [1], [3], [4], [5] while very limited number of studies focus on these areas on speech uttered in Arabic language [6], [7], [8], [9], [10]. One of the reasons of these few number of studies is the small number of accessible Arabic speech datasets in these areas [11], [12].…”

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confidence: 99%

“…They reported 96.3% accuracy to recognize the correct speaker [6]. Krobba et.al [7] investigated the effect of GSMEFR speech data on the performance of a "text-independent speaker identification system" based on "Gaussian Mixture Models (GMMs)" as classifiers. The recognition assessment was also performed using original "ARADIGIT sampled at 16 KHz and its 8 KHz down-sampled version".…”

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Emirati-accented speaker identification in each of neutral and shouted talking environments

Shahin

Nassif

Bahutair

2018

Int J Speech Technol

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This work is devoted to capturing Emirati-accented speech database (Arabic United Arab Emirates database) in each of neutral and shouted talking environments in order to study and enhance text-independent Emirati-accented "speaker identification performance in shouted environment" based on each of "First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s), Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s), and Third-Order Circular Suprasegmental Hidden Markov Models (CSPHMM3s)" as classifiers.In this research, our database was collected from fifty Emirati native speakers (twenty five per gender) uttering eight common Emirati sentences in each of neutral and shouted talking environments. The extracted features of our collected database are called "Mel-Frequency Cepstral Coefficients (MFCCs)". Our results show that average Emirati-accented speaker identification performance in neutral environment is 94.0%, 95.2%, and 95.9% based on CSPHMM1s, CSPHMM2s, and CSPHMM3s, respectively. On the other hand, the average performance in shouted environment is 51.3%, 55.5%, and 59.3% based, respectively, on "CSPHMM1s, CSPHMM2s, and CSPHMM3s". The achieved "average speaker identification performance in shouted environment based on CSPHMM3s" is very similar to that obtained in "subjective assessment by human listeners". 2

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Predicting Quranic Audio Clips Reciters Using Classical Machine Learning Algorithms: A Comparative Study

Elnagar

Lataifeh

2019

Studies in Computational Intelligence

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Evaluation of Speaker Identification System using GSMEFR speech Data

Cited by 19 publications

References 7 publications

A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments

A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments

Emirati-accented speaker identification in each of neutral and shouted talking environments

Predicting Quranic Audio Clips Reciters Using Classical Machine Learning Algorithms: A Comparative Study

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