VQ based comparative analysis of MFCC and BFCC speaker recognition system

Rehman, Faizan ur; Kumar, Chandar; Kumar, Shubash; Mehmood, Atif; Zafar, Umair

doi:10.1109/icict.2017.8320160

Cited by 7 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, such countermeasures often rely on standard time-frequency techniques constrained by assumptions such as stationarity or linearity of the underlying speech signal. The speech community has proposed multiple variations of these classical methods to overcome the aforementioned issues (see for example [16], [17], [18], [19], [20], [21]) and so dealing with different aspects faced by ASV systems in discriminating spoofed and real voices. The traditional practice foresees the extraction or engineering of the raw speech data features and then conducts the classification task by stacking them within a vector.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Mitigants for Speech Based Cyber Risk

Campi¹,

Peters

Azzaoui

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Statistical analysis of speech is an emerging area of machine learning. In this paper, we tackle the biometric challenge of Automatic Speaker Verification (ASV) of differentiating between samples generated by two distinct populations of utterances, those of an authentic human voice and those generated by a synthetic one. Solving such an issue through a statistical perspective foresees the definition of a decision rule function and a learning procedure to identify the optimal classifier. Classical state-ofthe-art countermeasures rely on strong assumptions such as stationarity or local-stationarity of speech that may be atypical to encounter in practice. We explore in this regard a robust non-linear and nonstationary signal decomposition method known as the Empirical Mode Decomposition combined with the Mel-Frequency Cepstral Coefficients in a novel fashion with a refined classifier technique known as multi-kernel Support Vector machine. We undertake significant real data case studies covering multiple ASV systems using different datasets, including the ASVSpoof 2019 challenge database. The obtained results overwhelmingly demonstrate the significance of our feature extraction and classifier approach versus existing conventional methods in reducing the threat of cyber-attack perpetrated by synthetic voice replication seeking unauthorised access.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning Mitigants for Speech Based Cyber Risk

Campi¹,

Peters

Azzaoui

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…An artificial neural network is used for classification. Comparative analysis of MFCC and Bark Frequency Cepstral Coefficient (BFCC) for speaker recognition system is discussed in [7]. The feature extraction for speech signals features is made by MFCC and BFCC for speaker recognition system with vector quantization method.…”

Section: Introductionmentioning

confidence: 99%

Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models

Sajeer*,

Rodrigues

2019

IJEAT

View full text Add to dashboard Cite

Identification of a person’s voice from the different voices is known as speaker recognition. The speech signals of individuals are selected by means of speaker recognition or identification. In this work, an efficient method for speaker recognition is made by using Discrete Wavelet Transform (DWT) features and Gaussian Mixture Models (GMM) for classification is presented. The input speech signal features are decomposed by DWT into subband coefficients. The DWT subband coefficient features are the input for the classification. Classification is made by GMM classifier at 4, 8, 16 and 32 Gaussian component levels. Results show a better accuracy of 96.18% speaker signals using DWT features and GMM classifier

show abstract

Two-level Noise Robust and Block Featured PNN Model for Speaker Recognition in Real Environment

Juneja

2022

Wireless Pers Commun

View full text Add to dashboard Cite

VQ based comparative analysis of MFCC and BFCC speaker recognition system

Cited by 7 publications

References 5 publications

Machine Learning Mitigants for Speech Based Cyber Risk

Machine Learning Mitigants for Speech Based Cyber Risk

Speaker Recognition System Based on Wavelet Features and Gaussian Mixture Models

Two-level Noise Robust and Block Featured PNN Model for Speaker Recognition in Real Environment

Contact Info

Product

Resources

About