Emirati-Accented Speaker Identification in Stressful Talking Conditions

Shahin, Ismail; Nassif, Ali Bou

doi:10.1109/icecta48151.2019.8959731

Cited by 7 publications

(2 citation statements)

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“…Results based on different classifiers and compensators as reported in our previous results, Refs. [31][32][33] reported speaker identification performance under shouted/stressful talking conditions using the Emirati accent dataset. Their reported speaker identification performance in shouted/stressful talking conditions was 58.6%, 61.1%, 65.0%, 68%, 74.6%, 75%, 78.4%, 81.7%, 78.7%, 83.4%, and 85.8% based, respectively, on "First-Order Hidden Markov Models (HMM1s), Second-Order Hidden Markov Models (HMM2s), Third-Order Hidden Markov Models (HMM3s), Second-Order Circular Hidden Markov Models (CHMM2s), First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTR-SPHMM1s), Suprasegmental Hidden Markov Models (SPHMMs), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s), Third-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM3s), 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)".…”

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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“…Imposters detection rate of 97.9% is reported in such work. Shahin and Nassif [10] have used a hidden Markov model (HMM) with a combination of MFCC for Arabic speech recognition. Tey evaluated their proposed structure using a dataset from 50 samples (25 females and 25 males).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Arabic Autoencoder Speech Recognition System for Electro-Larynx Device

Ameen

Kadhim

2023

Advances in Human-Computer Interaction

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Recent advances in speech recognition have achieved remarkable performance comparable with human transcribers’ abilities. But this significant performance is not the same for all the spoken languages. The Arabic language is one of them. Arabic speech recognition is bounded to the lack of suitable datasets. Artificial intelligence algorithms have shown promising capabilities for Arabic speech recognition. Arabic is the official language of 22 countries, and it has been estimated that 400 million people speak the Arabic language worldwide. Speech disabilities have been one of the expanding problems in the last decades, even in kids. Some devices can be used to generate speech for those people. One of these devices is the Servox Digital Electro-Larynx (EL). In this research, we developed an autoencoder with a combination of long short-term memory (LSTM) and gated recurrent units (GRU) models to recognize recorded signals from Servox Digital EL Electro-Larynx. The proposed framework consisted of three steps: denoising, feature extraction, and Arabic speech recognition. The experimental results show 95.31% accuracy for Arabic speech recognition with the proposed model. In this research, we evaluated different combinations of LSTM and GRU for constructing the best autoencoder. A rigorous evaluation process indicates better performance with the use of GRU in both encoder and decoder structures. The proposed model achieved a 4.69% word error rate (WER). Experimental results confirm that the proposed model can be used for developing a real-time app to recognize common Arabic spoken words.

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