Hybrid CNN-LSTM Speaker Identification Framework for Evaluating the Impact of Face Masks

Bader, Mohamed; Shahin, Ismail; Ahmed, Abdelfatah; Werghi, Naoufel

doi:10.1109/icecta57148.2022.9990138

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…Furthermore, studies have explored the modeling of reverberation using comb filtering, which is utilized to simulate room acoustics and evaluate the performance of speech processing algorithms in reverbration conditions [ 10 ]. Traditional speaker identification systems rely on extracting features from speech signals and matching them with reference models [ 4 ]. MFCCs have been a common choice for feature extraction due to their effectiveness in clean speech conditions.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Hassan,

Neyazi,

Seddeq

et al. 2024

PLoS ONE

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This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.

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Section: Related Workmentioning

confidence: 99%

“…SV finds applications in security contexts. Both SI and SV involve the creation of speaker models to be stored as references [ 4 , 5 ]. The process of SV is also referred to as speaker authentication, wherein the system either accepts or rejects the speaker’s identity claim.…”

Section: Introductionmentioning

confidence: 99%

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Hassan,

Neyazi,

Seddeq

et al. 2024

PLoS ONE

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“…In recent studies, a combination of CNN with variants of RNN has shown better performance in various areas. In the studies [40], [41] a hybrid network of CNN and LSTM models exhibited performance improvement in speaker verification and identification. Another study [42], employed a hybrid network of CNN and BiLSTM for language identification using spectrograms of speech and the results have shown improvement in existing works.…”

Section: Introductionmentioning

confidence: 99%

Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit

Anito,

Srinivasagan,

Jifara

et al. 2024

IJ-AI

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Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.

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“…This process significantly influences the precision of facial recognition systems by [8] minimising redundant data, expediting machine learning, and facilitating the development of computer models. However, facial feature extraction within recognition systems faces contemporary challenges, including variations in lighting [9], occlusions [10], and facial expressions [11], such as wearing face masks [12]. Persistent challenges in the field of computer vision and forensic science include lower accuracy rates and the lack of a precise Deep Learning (DL)-based image feature extraction approach for suspect identification [13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

Shree,

Mohapatra,

Suwanwiwat

et al. 2024

Preprint

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In suspect identification systems, facial features play a crucial role in recognising individuals. However, the challenge lies in sustaining the accuracy of the system over a long period of time, ensuring that it remains consistently high, reliable, and effective. This research introduces a novel lightweight model that requires low trainable parameters, a significantly smaller number than pre-trained models, which use millions of trainable parameters. The newly proposed recurrent feature iterative network integrates a convolutional neural network and long short-term memory in a single structure to synthesise diverse images and to effectively extract facial features. The long short-term memory-based feature-recurrent system demonstrates a significant improvement in accuracy when tested on the augmented reality face database, enhanced extended Yale B, Cohn-Kanade and extended Yale B datasets, achieving encouraging accuracy rates of 99.23%, 99.16%, 98.99%, and 97.68%, respectively. These accuracies, in general, outperform traditional baselines of 68.65%. This research advances the field by providing an innovative solution to enhance suspect identification systems through advanced facial image feature extraction, resulting in significantly improved accuracy rates.

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Hybrid CNN-LSTM Speaker Identification Framework for Evaluating the Impact of Face Masks

Cited by 8 publications

References 21 publications

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Speaker identification under noisy conditions using hybrid convolutional neural network and gated recurrent unit

A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

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