Error rate analyses of cooperative non-orthogonal multiple access (CNOMA) systems are of paramount importance to investigate the communication reliability for each user and facilitate the development of enhancement algorithms. Although CNOMA has recently attracted great attention, its error performance, particularly that of full-duplex cooperative NOMA (FD-CNOMA), is still underexplored in the literature. In this paper, we investigated the error performance of FD-CNOMA systems under imperfect successive interference cancellation (SIC) and residual self-interference (RSI), where new closed-form expressions of the exact bit error rates (BER) are derived for both users. Through the derived BER expressions, high-SNR analyses are conducted to show that FD-CNOMA has an error floor. Based on the derived expressions, we proposed a novel SINR-based selective FD-relaying, which minimizes the end-to-end (e2e) BER and improves the overall system performance. The analyses are extended to cover pulse-amplitude modulation (PAM) and quadrature-amplitude modulation (QAM) with arbitrary modulation orders. Monte Carlo simulations and numerical results are presented to corroborate the derived analytical expressions and give valuable insights into the error performance of FD-CNOMA systems.

This work presents a novel framework based on a discriminative auditory feature extractor for speaker recognition over VoIP network. The auditory model that simulates the mid-external and inner ear is incorporated into the conventional Mel Frequency Cepstral Coefficients (MFCC) scheme to produce new parameters that we named Ear Frequency Cepstral Coefficients (EFCCs). The experiments are conducted on the TIMIT corpus and the EFCC, used as input parameters tothe i-vector algorithm. Results showed interesting performances compared to the conventional MFCC parameters. In addition, this improvement was obtained by using fewer parameters (43 EFCCs vs 60 MFCCs) in our experiment.

Motivated by recent advances in speech and audio processing community reporting that incorporating the phase information can improve further the performance of state-of-the-art phase-independent features. We propose in this paper a modification in the extraction pipeline of the Mel-frequency Product Spectrum Cepstral Coefficients MFPSCC which warp the product spectrum with a Mel- Scale filterbank. The main novelty of this work resides in incorporating a Gammatone filterbank as a substitute of the Mel filterbank to reinforce the robustness of whole speaker verification system in noisy conditions. The proposed feature is dubbed the Gammatone Product-Spectrum Cepstral coefficients GPSCC. Experimental results are undertaken on the TIMIT corpus corrupted by different stationary and non-stationary noises using the GMMUBM de facto standard for speaker verification. Performance evaluations demonstrate drastic reduction in Equal Error Rates when using GPSCC compared to other related features and this gain in performance is more pronounced at low signal to noise ratios.

A big deal for current research on automatic speaker recognition is the effectiveness of the speaker modeling techniques for the talkers, because they have their own speaking style, depending on their specific accents and dialects. This paper investigates on the influence of the dialect and the size of database on the text independent speaker verification task using the SVM and the hybrid GMM/SVM speaker modeling. The Principal Component Analysis (PCA) technique is used in the front-end part of the speaker recognition system, in order to extract the most representative features. Experimental results show that the size of database has an important impact on the SVM and GMM/SVM based speaker verification performances, while the dialect has no significant effect. Applying PCA dimensionality reduction improves the recognition accuracy for both SVM and GMM/SVM based recognition systems. However, it did not give an obvious observation about the dialect effect.

One of the major problems in voiced communication systems is the presence of acoustic echoes generated from the coupling between the loudspeaker and the microphone. In this paper, a new method of Double-Talk Detection (DTD) for Acoustic Echo Cancellation (AEC), based on the Singular Value Decomposition (SVD), is proposed. Usually, the performances of the AEC, which is based on adaptive filtering, degrade seriously in the presence of speech issued from the near-end speaker (double-talk). Then, Double Talk Detection system must be added to AEC, for controlling the adaptation of the adaptive filter coefficients. For this purpose, we introduce the SVD of the far-end signal for detecting the double-talk periods. The obtained results, using TIMIT database, show that the proposed method outperforms the classical Geigel algorithm and Normalized Cross-Correlation (NCC) algorithm.