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This article presents systems that use speech technology, to emulate the one-on-one interaction a student can get from a virtual instructor. A web-based learning tool, the Learn IN Context (LINC+) system, designed and used in a real mixed-mode learning context for a computer (C++ language) programming course taught at the Université de Moncton (Canada) is described here. It integrates an Internet Voice Searching and Navigating (IVSN) system that helps learners to search and navigate both the web and their desktop environment through voice commands and dictation. LINC+ also incorporates an Automatic User Profile Building and Training (AUPB&T) module that allows users to increase speech recognition performance without having to go through the long and fastidious manual training process. The findings show that the majority of learners seem to be satisfied with this new media, and confirm that it does not negatively affect their cognitive load.

Microphone identification is a crucial challenge in the field of digital audio forensics. The ability to accurately identify the type of microphone used to record a piece of audio can provide important information for forensic analysis and crime investigations. In recent years, transformer-based deep-learning models have been shown to be effective in many different tasks. This paper proposes a system based on a transformer for microphone identification based on recorded audio. Two types of experiments were conducted: one to identify the model of the microphones and another in which identical microphones were identified within the same model. Furthermore, extensive experiments were performed to study the effects of different input types and sub-band frequencies on system accuracy. The proposed system is evaluated on the Audio Forensic Dataset for Digital Multimedia Forensics (AF-DB). The experimental results demonstrate that our model achieves state-of-the-art accuracy for inter-model and intra-model microphone classification with 5-fold cross-validation. [ABSTRACT FROM AUTHOR]

The goal of emotional voice conversion (EVC) is to convert the emotion of a speaker's voice from one state to another while maintaining the original speaker's identity and the linguistic substance of the message. Research on EVC in the Arabic language is well behind that conducted on languages with a wider distribution, such as English. The primary objective of this study is to determine whether Arabic emotions may be converted using a model trained for another language. In this work, we used an unsupervised many-to-many non-parallel generative adversarial network (GAN) voice conversion (VC) model called StarGANv2-VC to perform an Arabic EVC (A-EVC). The latter is realized by using pre-trained phoneme-level automatic speech recognition (ASR) and fundamental frequency (F0) models in the English language. The generated voice is evaluated by prosody and spectrum conversion in addition to automatic emotion recognition and speaker identification using a convolutional recurrent neural network (CRNN). The results of the evaluation indicated that male voices were scored higher than female voices and that the evaluation score for the conversion from neutral to other emotions was higher than the evaluation scores for the conversion of other emotions. [ABSTRACT FROM AUTHOR]

The automatic recognition of foreign-accented Arabic speech is a challenging task since it involves a large number of nonnative accents. As well, the nonnative speech data available for training are generally insufficient. Moreover, as compared to other languages, the Arabic language has sparked a relatively small number of research efforts. In this paper, we are concerned with the problem of nonnative speech in a speaker independent, large-vocabulary speech recognition system for modern standard Arabic (MSA). We analyze some major differences at the phonetic level in order to determine which phonemes have a significant part in the recognition performance for both native and nonnative speakers. Special attention is given to specific Arabic phonemes. The performance of an HMM-based Arabic speech recognition system is analyzed with respect to speaker gender and its native origin. The WestPoint modern standard Arabic database from the language data consortium (LDC) and the hidden Markov Model Toolkit (HTK) are used throughout all experiments. Our study shows that the best performance in the overall phoneme recognition is obtained when nonnative speakers are involved in both training and testing phases. This is not the case when a language model and phonetic lattice networks are incorporated in the system. At the phonetic level, the results show that female nonnative speakers perform better than nonnative male speakers, and that emphatic phonemes yield a significant decrease in performance when they are uttered by both male and female nonnative speakers.

In this paper, we present a Mixture Linear Prediction based approach for robust Gammatone Cepstral Coefficients extraction (MLPGCCs). The proposed method provides performance improvement of Automatic Speaker Verification (ASV) using i-vector and Gaussian Probabilistic Linear Discriminant Analysis GPLDA modeling under transmission channel noise. The performance of the extracted MLPGCCs was evaluated using the NIST 2008 database where a single channel microphone recorded conversational speech. The system is analyzed in the presence of different channel transmission noises such as Additive White Gaussian (AWGN) and Rayleigh fading at various Signals to Noise Ratio (SNR) levels. The evaluation results show that the MLPGCCs features are a promising way for the ASV task. Indeed, the speaker verification performance using the MLPGCCs proposed features is significantly improved compared to the conventional Gammatone Frequency Cepstral Coefficients (GFCCs) and Mel Frequency Cepstral Coefficients (MFCCs) features. For speech signals corrupted with AWGN noise at SNRs ranging from (-5 dB to 15 dB), we obtain a significant reduction of the Equal Error Rate (EER) ranging from 9.41% to 6.65% and 3.72% to 1.50%, compared with conventional MFCCs and GFCCs features respectively. In addition, when the test speech signals are corrupted with Rayleigh fading channel we achieve an EER reduction ranging from 23.63% to 7.8% and from 10.88% to 6.8% compared with conventional MFCCs and GFCCs, respectively. We also found that the combination of GFCCs and MLPGCCs gives the highest performance of speaker verification system. The best performance combination achieved is around EER from 0.43% to 0.59% and 1.92% to 3.88%. [ABSTRACT FROM AUTHOR]

The system combining i-vector and probabilistic linear discriminant analysis (PLDA) has been applied with great success in the speaker recognition task. The i-vector space gives a low-dimensional representation of a speech segment and training data of a PLDA model, which offers greater robustness under different conditions. In this paper, we propose a new framework based on i-vector/PLDA and Maximum Entropy (ME) to improve the performance of speaker identification system in the presence of speech coding distortion. The results are reported on TIMIT database and speech coding obtained by passing the speech test from TIMIT database through the AMR encoder/decoder. Our results show that the proposed methode achieves improved performance when compared with the i-vector/PLDA and MEGMM. [ABSTRACT FROM AUTHOR]

This paper presents a new offline face recognition system. The proposed system is built on one dimensional left-toright Hidden Markov Models (1D-HMMs). Facial image features are extracted using Gabor wavelets. The dimensionality of these features is reduced using the Fisher's Discriminant Analysis method to keep only the most relevant information. Unlike existing techniques using 1D-HMMs, in classification step, the proposed system employs 1D-HMMs to find the relationship between reduced features components directly without any additional segmentation step of interest regions in the face image. The performance evaluation of the proposed method was performed with AR database and the proposed method showed a high recognition rate for this database. [ABSTRACT FROM AUTHOR]