A Word-Dependent Automatic Arabic Speaker Identification System

AlDahri, Sulaiman S.; Al-Jassar, Y.H.; Alotaibi, Yousef Ajami; Alsulaiman, Mansour; Abdullah-Al-Mamun, Khondaker

doi:10.1109/isspit.2008.4775669

Cited by 15 publications

(7 citation statements)

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“…Speech features are extracted using MFCC, and HTK is used to implement the speaker identification module with phoneme based HMM. The designed automatic Arabic speaker identification system contains 100 speakers and it achieved 96.25% accuracy in recognizing the correct speaker [7].…”

Section: Related Workmentioning

confidence: 99%

Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques

Mengistu

Alemayehu

2017

IJEECS

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Section: Related Workmentioning

confidence: 99%

Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques

Mengistu

Alemayehu

2017

IJEECS

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“…This research is conducted with a local database recorded at King Saud University, College of Computer and Information Sciences (CCIS), during the year 2007 [15]. The database consists of 91 native Arabic speakers, pronouncing the Arabic word ‫"نعم"‬ (/n/, /a/, /ʕ/, /a/, /m/), which stands for the word "yes" in English, in 5 different occurrences (samples).…”

Section: Speech Corpusmentioning

confidence: 99%

Automatic enlargement of speech corpus for speaker recognition

Alsulaiman

2011

2011 IEEE Symposium on Computers &Amp; Informatics

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This research deals with the problem of recognition when only a few samples are available for training of the system. To avoid the low recognition rate caused by such type of speech corpus, automatic techniques for the enlargement of speech corpus are proposed in this paper. These techniques are: lengthening of sample by automatic segmentation, automatic noise addition at different sound-to-noise ratios (SNRs), and lengthening of reversed sample. Different combinations of samples, generated by the proposed techniques, are used to obtain the high recognition rate. These techniques have shown promising result.

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“…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%

“…One of the reasons of these few number of studies is the small number of accessible Arabic speech datasets in these areas [11], [12]. Al-Dahri et.al [6] studied word-dependent "speaker identification systems" encompassing 100 speakers speaking Arabic isolated words based on "Hidden Markov Models (HMMs)". "Mel-Frequency Cepstral Coefficients (MFCCs)" have been adopted as the extracted features of the utilized dataset.…”

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confidence: 99%

“…"Mel-Frequency Cepstral Coefficients (MFCCs)" have been adopted as the extracted features of the utilized dataset. 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.…”

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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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A Word-Dependent Automatic Arabic Speaker Identification System

Cited by 15 publications

References 15 publications

Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques

Text Independent Amharic Language Speaker Identification in Noisy Environments using Speech Processing Techniques

Automatic enlargement of speech corpus for speaker recognition

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

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