Hybrid Feature Extraction MFCC and Feature Selection CNN for Speaker Identification Using CNN: A Comparative Study

Abdulqader, Aya Hasan; Al-Haddad, S. A. R.; Abdo, Salah; Abdulghani, Amer; Natarajan, Sureshkumar

doi:10.1109/esmarta56775.2022.9935422

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…MFCCs have been a common choice for feature extraction due to their effectiveness in clean speech conditions. However, their robustness in the presence of reverberation and noise is a subject of ongoing investigation [ 3 , 6 ].…”

Section: Related Workmentioning

confidence: 99%

“…If the system denies access to an enrolled speaker’s utterance, the speaker is classified as an impostor. Consequently, SV systems play a crucial role in security applications, thwarting unauthorized entry by individuals [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…During training, features of new speakers are extracted and recorded in the database, while recognition involves extracting features for unknown speakers to determine their identities. Mel-Frequency Cepstral Coefficients (MFCCs), widely acclaimed for their robustness in representing clean speech, are the favored features [ 3 , 6 ]. However, their robustness diminishes in cases of degraded speech quality.…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…The paper also addresses the challenges faced by speaker recognition systems, including domain robustness, the impact of noise, and limitations in neural network architecture. It highlights the importance of utilizing different CNN architectures, feature extraction techniques, and training methods to enhance system performance [9]. In summary, the proposed methodology focuses on overcoming research gaps related to small datasets, loss functions, acoustic conditions, and domain robustness in speaker recognition.…”

Section: Objectivesmentioning

confidence: 99%

Exploring the Impact of Mismatch Conditions, Noisy Backgrounds, and Speaker Health on Convolutional Autoencoder-Based Speaker Recognition System with Limited Dataset

Niwatkar,

Kanse,

Kushwaha

2024

ICST Transactions on Scalable Information Systems

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This paper presents a novel approach to enhance the success rate and accuracy of speaker recognition and identification systems. The methodology involves employing data augmentation techniques to enrich a small dataset with audio recordings from five speakers, covering both male and female voices. Python programming language is utilized for data processing, and a convolutional autoencoder is chosen as the model. Spectrograms are used to convert speech signals into images, serving as input for training the autoencoder. The developed speaker recognition system is compared against traditional systems relying on the MFCC feature extraction technique. In addition to addressing the challenges of a small dataset, the paper explores the impact of a "mismatch condition" by using different time durations of the audio signal during both training and testing phases. Through experiments involving various activation and loss functions, the optimal pair for the small dataset is identified, resulting in a high success rate of 92.4% in matched conditions. Traditionally, Mel-Frequency Cepstral Coefficients (MFCC) have been widely used for this purpose. However, the COVID-19 pandemic has drawn attention to the virus's impact on the human body, particularly on areas relevant to speech, such as the chest, throat, vocal cords, and related regions. COVID-19 symptoms, such as coughing, breathing difficulties, and throat swelling, raise questions about the influence of the virus on MFCC, pitch, jitter, and shimmer features. Therefore, this research aims to investigate and understand the potential effects of COVID-19 on these crucial features, contributing valuable insights to the development of robust speaker recognition systems.

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Speaker Recognition Using Convolutional Autoencoder in Mismatch Condition with Small Dataset in Noisy Background

Niwatkar,

Kanse,

Kushwaha

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Hybrid Feature Extraction MFCC and Feature Selection CNN for Speaker Identification Using CNN: A Comparative Study

Cited by 3 publications

References 34 publications

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Exploring the Impact of Mismatch Conditions, Noisy Backgrounds, and Speaker Health on Convolutional Autoencoder-Based Speaker Recognition System with Limited Dataset

Speaker Recognition Using Convolutional Autoencoder in Mismatch Condition with Small Dataset in Noisy Background

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