Automatic Speaker Verification using Gammatone Frequency Cepstral Coefficients

Choudhary, Himanshu; Sadhya, Debanjan; Patel, Vinal

doi:10.1109/spin52536.2021.9566150

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Remarkably, the temporal profiles of specific cepstral coefficient sets consistently demonstrate analogous behaviors in both training and testing, despite variations in coefficient amplitudes across these stages. This underscores the constancy in the temporal forms of selected cepstral coefficients from training to testing [ 29 ].…”

Section: Feature Extraction Stagesmentioning

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: Feature Extraction Stagesmentioning

confidence: 99%

“…ANNs serve as simulation models for the human brain functions, emulating the brain capacity to perform complex tasks by processing data in a manner akin to human cognition [ 29 , 30 ]. Structured with an assembly of numerous simple processing units known as neurons, ANNs are interlinked through connections denoted as weights.…”

Section: Classification Processmentioning

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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show abstract

“…However, the issue of noise remains open with MFCC and CQCC features. In [31], authors used GTCC feature and pitch at front-end for feature extraction, and passed these features to GMM and KNN to improve the performance of ASV system. Kaun et al [10] applied auditory based BFCC features with AURORA 2 dataset, and compared these features' performance with MFCC using HMM model.…”

Section: A Related Workmentioning

confidence: 99%

Spoof Detection using Sequentially Integrated Image and Audio Features

Chakravarty¹,

Dua²

2023

IJCDS

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Analyzing the intricate nature of an audio signal often requires the extraction of relevant features, which serve as informative descriptors of the signal. It entails studying the signal and determining how signals are related to one another. As a result, the performance of audio spoofing detection in Automatic Speaker Verification (ASV) systems is strongly reliant on front-end feature extraction. In this paper, three types of successively integrated features have been proposed. First, Acoustic Ternary Pattern (ATP) image features are sequentially fused with different audio features such as MFCC, CQCC, GTCC, BFCC and PLP, individually. Second, LBP image features are combined with all these audio features similarly. Then, the sequential integration of ATP-LBP features is combined individually with MFCC, CQCC, GTCC, BFCC and PLP features. Finally, these front-end hybrid feature sets are classified using different ML and deep learning algorithms based acoustic models at the back-end. The state-of-the-art ASVspoof 2019 dataset has been used to implement various front-end and back-end combinations. The research outcomes reveal that the proposed approach achieved the best results with ATP-LBP-GTCC at the front end with LSTM-based acoustic model at the back-end.

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“…Another study in [25], proposed a speaker identification system for forensic applications using the GMM with universal background model (GMM-UBM) and GFCC features in noisy environments. In the reference [26] a GMM method and GFCC feature were employed for speaker verification using datasets with real-world noises.…”

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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Automatic Speaker Verification using Gammatone Frequency Cepstral Coefficients

Cited by 5 publications

References 14 publications

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs

Spoof Detection using Sequentially Integrated Image and Audio Features

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

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