Enhanced forensic speaker verification performance using the ICA-EBM algorithm under noisy and reverberant environments

Al-Ali, Ahmed Kamil Hasan; Chandran, Vinod; Naik, Ganesh R.

doi:10.1007/s12065-020-00406-8

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…The most cited methods are the Cepstral Mean Subtraction (CMS) [18], the Power-Normalized Cepstral Coefficients (PNCCs) [19], and the Cepstral Mean Normalization (CMN) [20] which is a popular feature compensation method dealing with convolutional noise. In this same context, the majority of the published works demonstrated that the wavelet-based feature extraction [21][22][23][24] has better performance improvement than traditional Cepstral features in noisy environments. The already presented wavelet-based techniques rely on the multiresolution PWP properties and combine the extracted MFCC features from various frequency sub bands to a unique feature vector.…”

Section: Introductionmentioning

confidence: 98%

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Helali

Hajaiej

Chérif

2020

Eng. Technol. Appl. Sci. Res.

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The real-time performance of Automatic Speech Recognition (ASR) is a big challenge and needs high computing capability and exhaustive memory consumption. Getting a robust performance against inevitable various difficult situations such as speaker variations, accents, and noise is a tedious task. It’s crucial to expand new and efficient approaches for speech signal extraction features and pre-processing. In order to fix the high dependency issue related to processing succeeding steps in ARS and enhance the extracted features’ quality, noise robustness can be solved within the ARS extraction block feature, removing implicitly the need for further additional specific compensation parameters or data collection. This paper proposes a new robust acoustic extraction approach development based on a hybrid technique consisting of Perceptual Wavelet Packet (PWP) and Mel Frequency Cepstral Coefficients (MFCCs). The proposed system was implemented on a Rasberry Pi board and its performance was checked in a clean environment, reaching 99% average accuracy. The recognition rate was improved (from 80% to 99%) for the majority of Signal-to-Noise Ratios (SNRs) under real noisy conditions for positive SNRs and considerably improved results especially for negative SNRs.

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Section: Introductionmentioning

confidence: 98%

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Helali

Hajaiej

Chérif

2020

Eng. Technol. Appl. Sci. Res.

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Effect of identical twins on deep speaker embeddings based forensic voice comparison

Abed,

Sztahó

2024

Int J Speech Technol

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Deep learning has gained widespread adoption in forensic voice comparison in recent years. It is mainly used to learn speaker representations, known as embedding features or vectors. In this work, the effect of identical twins on two state-of-the-art deep speaker embedding methods was investigated with special focus on metrics of forensic voice comparison. The speaker verification performance has been assessed using the likelihood-ratio framework by likelihood ratio cost and equal error rate. The AVTD twin speech dataset was applied. The results show a significant reduction in speaker verification performance when twin samples are present. Neither the adaptation of LR score calculation to twin samples, nor fine-tuning the pre-trained speaker embedding models seemed to be able to leverage this limitation. It was found that the recognition of same or different speakers was possible even in the case of identical twins but the performance dropped greatly. The lowest EER of the best performing model was 3.4% in the case of non-twin; at the same time, EER was 25.3% when twins were present. This doesn’t mean that the presented methods are useless in case of identical twins, but it must be taken into consideration that in case of a higher likelihood-ratio score (which indicates same speakers on the tested samples), the possibility of twins must also be considered in a real casework.

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Enhancing Forensic Voice Analysis with the Aid of Noise Cancellation: A Forensic Approach

Sheoran,

Mahna

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Enhanced forensic speaker verification performance using the ICA-EBM algorithm under noisy and reverberant environments

Cited by 3 publications

References 43 publications

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM

Effect of identical twins on deep speaker embeddings based forensic voice comparison

Enhancing Forensic Voice Analysis with the Aid of Noise Cancellation: A Forensic Approach

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