A Comparison Between MFCC and MSE Features for Text-Independent Speaker Recognition Using Machine Learning Algorithms

Ramírez-Hernández, Joseph Isaac; Manzo-Martínez, Alain; Gaxiola, Fernando; González-Gurrola, Luis Carlos; Álvarez-Oliva, Vania C.; López-Santillán, Roberto

doi:10.1007/978-3-031-22042-5_7

Studies in Computational Intelligence

2023

DOI: 10.1007/978-3-031-22042-5_7

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A Comparison Between MFCC and MSE Features for Text-Independent Speaker Recognition Using Machine Learning Algorithms

Joseph Isaac Ramírez-Hernández

Alain Manzo-Martínez

Fernando Gaxiola

et al.

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2024

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Cited by 2 publications

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A novel speaker verification approach featuring multidomain acoustics based on the weighted city block Minkowski distance

Jha,

Srivastava,

Jain

2024

ETRI Journal

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Access control is vital in interconnected environments like the Internet of Things, Industry 4.0, and smart connectivity, ensuring authorized access for security. Biometric‐based access, particularly speaker verification (SV), enhances security with unique vocal features, offering nonintrusive authentication with continuous monitoring. Single‐domain features prove insufficient in distinguishing similar traits, prompting latest SV advancements to adopt multidomain‐based speech features. This paradigm addresses the limitations of single‐domain features by amalgamating the merits of individual domains, establishing a cutting‐edge approach. It utilizes cepstral–frequency–time domain feature fusion, achieved via cepstral mean‐variance normalization for generalizability. The weighted city block Minkowski distance is proposed to compare reference and test speech templates. Parameters are computed based on the confusion matrix, template matching distance functions, dynamic acoustic conditions, and additive white Gaussian noise. A deep convolutional neural network classifier is assessed on open‐source LibriSpeech and Speaker in the Wild corpora, surpassing the current methodologies.

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A novel speaker verification approach featuring multidomain acoustics based on the weighted city block Minkowski distance

Jha,

Srivastava,

Jain

2024

ETRI Journal

View full text Add to dashboard Cite

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Unified voice analysis: speaker recognition, age group and gender estimation using spectral features and machine learning classifiers

Akgün,

Sadık

2024

Journal of Scientific Reports-A

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Predicting speaker's personal traits from voice data has been a subject of attention in many fields such as forensic cases, automatic voice response systems, and biomedical applications. Within the scope of this study, gender and age group prediction was made with the voice data recorded from 24 volunteers. Mel-frequency cepstral coefficients (MFCC) were extracted from the audio data as hybrid time/frequency domain features, and fundamental frequencies and formants were extracted as frequency domain features. These obtained features were fused in a feature pool and age group and gender estimation studies were carried out with 4 different machine learning algorithms. According to the results obtained, the age groups of the participants could be classified with 93% accuracy and the genders with 99% accuracy with the Support Vector Machines algorithm. Also, speaker recognition task was successfully completed with 93% accuracy with the Support Vector Machines.

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A Comparison Between MFCC and MSE Features for Text-Independent Speaker Recognition Using Machine Learning Algorithms

Cited by 2 publications

References 12 publications

A novel speaker verification approach featuring multidomain acoustics based on the weighted city block Minkowski distance

A novel speaker verification approach featuring multidomain acoustics based on the weighted city block Minkowski distance

Unified voice analysis: speaker recognition, age group and gender estimation using spectral features and machine learning classifiers

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