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Noise robust automatic speaker verification systems: review and analysis

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Abstract

Like any other biometric systems, Automatic Speaker Verification (ASV) systems are also vulnerable to the spoofing attacks. Hence, it is important to develop the countermeasures in order to handle these attacks. In spoofing mainly two types of attacks are considered, logical access attacks and presentation attacks. In the last few decades, several systems have been proposed by various researchers for handling these kinds of attacks. However, noise handling capability of ASV systems is of major concern, as the presence of noise may make an ASV system to falsely evaluate the original human voice as the spoofed audio. Hence, the main objective of this paper is to review and analyze the various noise robust ASV systems proposed by different researchers in recent years. The paper discusses the various front end and back-end approaches that have been used to develop these systems with putting emphasis on the noise handling techniques. Various kinds of noises such as babble, white, background noises, pop noise, channel noises etc. affect the development of an ASV system. This survey starts with discussion about the various components of ASV system. Then, the paper classifies and discusses various enhanced front end feature extraction techniques like phase based, deep learning based, magnitude-based feature extraction techniques etc., which have been proven to be robust in handling noise. Secondly, the survey highlights the various deep learning and other baseline models that are used in backend, for classification of the audio correctly. Finally, it highlights the challenges and issues that still exist in noise handling and detection, while developing noise robust ASV systems. Therefore, on the basis of the proposed survey it can be interpreted that the noise robustness of ASV system is the challenging issue. Hence the researchers should consider the robustness of ASV against noise along with spoofing attacks.

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Data availability

Data sharing and Code availability not applicable to this article as no datasets were generated during current study. This study did not use the individual’s data or images.

Abbreviations

MFCC:

Mel frequency cepstral coefficients

LPCC:

Linear predictive cepstral coefficients

PLP:

Perceptual linear prediction

GMM:

Gaussian mixture models

UBM:

Universal background model

SVM:

Support vector machine

PLDA:

Probabilistic linear discriminant analysis

MCEP:

Mel-cepstral coefficients

EER:

Equal error rate

FAR:

False acceptance rate

FRR:

False rejection rate

FFT:

Fast Fourier Transform

CNPCC:

Cosine normalized phase-based cepstral coefficients

LPRC:

LP residual Cepstral Coefficients

DNN:

Deep neural network

VAD:

Voice activity detection

STFT:

Short-time Fourier Transform

LMS:

Log magnitude spectrum

RLMS:

Residual log magnitude spectrum

IF:

Instantaneous frequency derivative

BPD:

Baseband phase difference

GD:

Group delay

MGD:

Modified group delay

MLP:

Multi-layer perceptron

SCMC:

Subband spectral centroid magnitude

MHEC:

Mean Hilbert envelope coefficient

RPS:

Relative phase shift

DBN:

Deep belief network

LFCC:

Linear frequency cepstral coefficients

IIR-CQT:

Infinite impulse response—constant Q transform

CQCC:

Constant-Q Cepstral Coefficients

IMFCC:

Inverse Mel frequency Cepstral Coefficients

CNN:

Convolutional neural network

RNN:

Recurrent neural network

LSTM:

Long short term memory

HMM:

Hidden Markov model

STCC:

Short term Cepstral coefficients

CMVN:

Cepstral mean variance normalization

MSRCC:

Magnitude based spectral root Cepstral coefficients

PSRCC:

Phase based spectral root Cepstral coefficients

CFCC-IF:

Cochlear filter Cepstral coefficient instantaneous frequency

CLDNN:

Convolutional LSTM Neural Network

HFCC:

High frequency Cepstral Coefficients

LCNN:

Light convolutional neural network

GRCNN:

Gated recurrent convolutional neural networks

LDA:

Linear discriminant analysis

CGAN:

Conditional generative adversarial networks

TECC:

Teager energy Cepstral coefficients

ResNet:

Residual network

TDNN:

Time-delayed neural network

DCF:

Detection cost function

IMF:

Intrinsic mode functions

HTER:

Half total equal error rate

SDER:

Spoofing detection error rate

EMD:

Empirical mode decomposition

FBCC:

Filter-based Cepstral Coefficient

VAD:

Voice activity detection

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  1. Department of Computer Engineering, National Institute of Technology, Kurukshetra, India

    Sanil Joshi & Mohit Dua

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  1. Sanil Joshi
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Both the authors have equal contribution in preparing manuscript. This study is the authors own original work, which has not been previously published elsewhere. All authors implemented the proposed idea. I, Sanil Joshi, wrote the manuscript, including tables and figures, and Dr Mohit Dua reviewed the manuscript.

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Correspondence to Sanil Joshi.

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Joshi, S., Dua, M. Noise robust automatic speaker verification systems: review and analysis. Telecommun Syst 87, 845–886 (2024). https://doi.org/10.1007/s11235-024-01212-8

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