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

© 2013 IEEE. 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

Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions

© 2017 IEEE. The performance of forensic speaker verification degrades severely in the presence of high levels of environmental noise and reverberation conditions. Multiple channel speech enhancement algorithms are a possible solution to reduce the effect of environmental noise from the noisy speech signals. Although multiple speech enhancement algorithms such as multi-run independent component analysis (ICA) were used in previous studies to improve the performance of recognition in biosignal applications, the effectiveness of multi-run ICA algorithm to improve the performance of noisy forensic speaker verification under reverberation conditions has not been investigated yet. In this paper, the multi-run ICA algorithm is used to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times. Wavelet-based mel frequency cepstral coefficients (MFCCs) feature warping approach is applied to the enhanced speech signals to extract the robust features to environmental noise and reverberation conditions. The state-of-The-Art intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) are used as a classifier in our approach. Experimental results show that forensic speaker verification based on the multi-run ICA algorithm achieves significant improvements in equal error rate (EER) of 60.88%, 51.84%, 66.15% over the baseline noisy speaker verification when enrolment speech signals reverberated at 0.15 sec and the test speech signals were mixed with STREET, CAR and HOME noises respectively at-10 dB signal to noise ratio (SNR)