Forensic Speaker Verification Research Articles

Real-time adaptive training for forensic speaker verification in reverberation conditions

Real-time adaptive training for forensic speaker verification in reverberation conditions

Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification Model

While speaker verification represents a critically important application of speaker recognition, it is also the most challenging and least well-understood application. Robust feature extraction plays an integral role in enhancing the efficiency of forensic speaker verification. Although the speech signal is a continuous one-dimensional time series, most recent models depend on recurrent neural network (RNN) or convolutional neural network (CNN) models, which are not able to exhaustively represent human speech, thus opening themselves up to speech forgery. As a result, to accurately simulate human speech and to further ensure speaker authenticity, we must establish a reliable technique. This research article presents a Two-Tier Feature Extraction with Metaheuristics-Based Automated Forensic Speaker Verification (TTFEM-AFSV) model, which aims to overcome the limitations of the previous models. The TTFEM-AFSV model focuses on verifying speakers in forensic applications by exploiting the average median filtering (AMF) technique to discard the noise in speech signals. Subsequently, the MFCC and spectrograms are considered as the inputs to the deep convolutional neural network-based Inception v3 model, and the Ant Lion Optimizer (ALO) algorithm is utilized to fine-tune the hyperparameters related to the Inception v3 model. Finally, a long short-term memory with a recurrent neural network (LSTM-RNN) mechanism is employed as a classifier for automated speaker recognition. The performance validation of the TTFEM-AFSV model was tested in a series of experiments. Comparative study revealed the significantly improved performance of the TTFEM-AFSV model over recent approaches.

Generalized End-to-End Loss for Forensic Speaker Verification

Generalized End-to-End Loss for Forensic Speaker Verification

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

Forensic speaker verification performance reduces significantly under high levels of noise and reverberation. Multiple channel speech enhancement algorithms, such as independent component analysis by entropy bound minimization (ICA-EBM), can be used to improve noisy forensic speaker verification performance. Although the ICA-EBM was used in previous studies to separate mixed speech signals under clean conditions, the effectiveness of using the ICA-EBM for improving forensic speaker verification performance under noisy and reverberant conditions has not been investigated yet. In this paper, the ICA-EBM algorithm is used to separate the clean speech from noisy speech signals. Features from the enhanced speech are obtained by combining the feature-warped mel frequency cepstral coefficients with similar features extracted from the discrete wavelet transform. The identity vector (i-vector) length normalized Gaussian probabilistic linear discriminant analysis is used as a classifier. The Australian Forensic Voice Comparison and QUT-NOISE corpora were used to evaluate forensic speaker verification performance under noisy and reverberant conditions. Simulation results demonstrate that forensic speaker verification performance based on ICA-EBM improves compared with that of the traditional independent component analysis under different types of noise and reverberation environments. For surveillance recordings corrupted with different types of noise (CAR, STREET and HOME) at − 10 dB signal to noise ratio, the average equal error rate of the proposed method based on ICA-EBM is better than that of the traditional ICA by 12.68% when the interview recordings are kept clean, and 7.25% when the interview recordings have simulated room reverberations.

Forensic Speaker Verification Using Ordinary Least Squares.

In Brazil, the recognition of speakers for forensic purposes still relies on a subjectivity-based decision-making process through a results analysis of untrustworthy techniques. Owing to the lack of a voice database, speaker verification is currently applied to samples specifically collected for confrontation. However, speaker comparative analysis via contested discourse requires the collection of an excessive amount of voice samples for a series of individuals. Further, the recognition system must inform who is the most compatible with the contested voice from pre-selected individuals. Accordingly, this paper proposes using a combination of linear predictive coding (LPC) and ordinary least squares (OLS) as a speaker verification tool for forensic analysis. The proposed recognition technique establishes confidence and similarity upon which to base forensic reports, indicating verification of the speaker of the contested discourse. Therefore, in this paper, an accurate, quick, alternative method to help verify the speaker is contributed. After running seven different tests, this study preliminarily achieved a hit rate of 100% considering a limited dataset (Brazilian Portuguese). Furthermore, the developed method extracts a larger number of formants, which are indispensable for statistical comparisons via OLS. The proposed framework is robust at certain levels of noise, for sentences with the suppression of word changes, and with different quality or even meaningful audio time differences.

Forensic Linguistic Inquiry into the Validity of F0 as Discriminatory Potential in the System of Forensic Speaker Verification

Forensic Linguistic Inquiry into the Validity of F0 as Discriminatory Potential in the System of Forensic Speaker Verification

Enhanced Forensic Speaker Verification Using a Combination of DWT and MFCC Feature Warping in the Presence of Noise and Reverberation Conditions

Environmental noise and reverberation conditions severely degrade the performance of forensic speaker verification. Robust feature extraction plays an important role in improving forensic speaker verification performance. This paper investigates the effectiveness of combining features, mel frequency cepstral coefficients (MFCCs), and MFCC extracted from the discrete wavelet transform (DWT) of the speech, with and without feature warping for improving modern identity-vector (i-vector)-based speaker verification performance in the presence of noise and reverberation. The performance of i-vector speaker verification was evaluated using different feature extraction techniques: MFCC, feature-warped MFCC, DWT-MFCC, feature-warped DWT-MFCC, a fusion of DWT-MFCC and MFCC features, and fusion feature-warped DWT-MFCC and feature-warped MFCC features. We evaluated the performance of i-vector speaker verification using the Australian Forensic Voice Comparison and QUT-NOISE databases in the presence of noise, reverberation, and noisy and reverberation conditions. Our results indicate that the fusion of feature-warped DWT-MFCC and feature-warped MFCC is superior to other feature extraction techniques in the presence of environmental noise under the majority of signal-to-noise ratios (SNRs), reverberation, and noisy and reverberation conditions. At 0-dB SNR, the performance of the fusion of feature-warped DWT-MFCC and feature-warped MFCC approach achieves a reduction in average equal error rate of 21.33%, 20.00%, and 13.28% over feature-warped MFCC, respectively, in the presence of various types of environmental noises only, reverberation, and noisy and reverberation environments. The approach can be used for improving the performance of forensic speaker verification and it may be utilized for preparing legal evidence in court.

The use of standardized-normalized cross VQ-distortion for forensic speaker verification

The use of standardized-normalized cross VQ-distortion for forensic speaker verification