A new method to secure speech communication using the discrete wavelet transforms (DWT) and the fast Fourier transform is presented in this article. In the first phase of the hiding technique, we separate the speech high-frequency components from the low-frequency components using the DWT. In a second phase, we exploit the low-pass spectral proprieties of the speech spectrum to hide another secret speech signal in the low-amplitude high-frequency regions of the cover speech signal. The proposed method allows hiding a large amount of secret information while rendering the steganalysis more complex. Experimental results prove the efficiency of the proposed hiding technique since the stego signals are perceptually indistinguishable from the equivalent cover signal, while being able to recover the secret speech message with slight degradation in the quality.

Feature extraction is a critical stage of digital speech processing systems. Quality of features is of great importance to provide a solid foundation upon which the subsequent stages stand. Distinctive phonetic features (DPFs) are one of the most representative features of the speech signals. The significance of DPFs is in their ability to provide abstract description of the places and manners of articulation of the language phonemes. A phoneme's DPF element reflects unique articulatory information about that phoneme. Therefore, there is a need to discover and investigate each DPF element individually in order to achieve a deeper understanding and to come up with a descriptive model for each one. Such fine-grained modeling will satisfy the uniqueness of each DPF element. In this paper, the problem of DPF modeling and extraction of modern standard Arabic is tackled. Due to the remarkable success of deep neural networks (DNNs) that are initialized using deep belief networks (DBNs) in serving DSP applications and its capability of extracting highly representative features from the raw data, we exploit its modeling power to investigate and model the DPF elements. DNN models are compared with the classical multilayer perceptron (MLP) models. The representativeness of several acoustic cues for different DPF elements was also measured. This paper is based on formalizing DPF modeling problem as a binary classification problem. Because the DPF elements are highly imbalanced data, evaluating the quality of models is a very tricky process. This paper addresses the proper evaluation measures satisfying the imbalanced nature of the DPF elements. After modeling each element individually, the two top-level DPF extractors are designed: MLP-and DNN-based extractors. The results show the quality of DNN models and their superiority over MLPs with accuracies of 89.0% and 86.7%, respectively.INDEX TERMS Modern standard Arabic, distinctive phonetic features, speech processing, deep belief networks, restricted Boltzmann machine.YASSER SEDDIQ received the B.S. degree in computer engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, in 2004, and the M.S. degree in computer engineering and the Ph.D. degree in computer and information sciences (computer engineering) from King Saud University (KSU), Riyadh, Saudi Arabia, in 2010 and 2017, respectively. He is currently an Assistant Research Professor with the King Abdulaziz City for Science and Technology (KACST), Riyadh. His research interests include digital signal processing, speech processing, image processing, computer arithmetic, and digital systems design using FPGA.

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Recommended by Juan I. Godino-Llorente Assistive speech-enabled systems are proposed to help both French and English speaking persons with various speech disorders. The proposed assistive systems use automatic speech recognition (ASR) and speech synthesis in order to enhance the quality of communication. These systems aim at improving the intelligibility of pathologic speech making it as natural as possible and close to the original voice of the speaker. The resynthesized utterances use new basic units, a new concatenating algorithm and a grafting technique to correct the poorly pronounced phonemes. The ASR responses are uttered by the new speech synthesis system in order to convey an intelligible message to listeners. Experiments involving four American speakers with severe dysarthria and two Acadian French speakers with sound substitution disorders (SSDs) are carried out to demonstrate the efficiency of the proposed methods. An improvement of the Perceptual Evaluation of the Speech Quality (PESQ) value of 5% and more than 20% is achieved by the speech synthesis systems that deal with SSD and dysarthria, respectively.

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Distinctive phonetic features have an important role in Arabic speech phoneme recognition. In a given language, distinctive phonetic features are extrapolated from acoustic features using different methods. However, exploiting lengthy acoustic features vector in the sake of phoneme recognition has a huge cost in terms of computational complexity, which in turn, affects real time applications. The aim of this work is to consider methods to reduce the size of features vector employed for distinctive phonetic feature and phoneme recognition. The objective is to select the relevant input features that contribute to the speech recognition process. This, in turn, will lead to a reduced computational complexity of recognition algorithm, and an improved recognition accuracy. In the proposed approach, genetic algorithm is used to perform optimal features selection. Therefore, a baseline model based on feedforward neural networks is first built. This model is used to benchmark the results of proposed features selection method with a method that employs all elements of a features vector. Experimental results, utilizing the King Abdulaziz City for Science and Technology Arabic Phonetic Database, show that the average genetic algorithm based phoneme overall recognition accuracy is maintained slightly higher than that of recognition method employing the full-fledge features vector. The genetic algorithm based distinctive phonetic features recognition method has achieved a 50% reduction in the dimension of the input vector while obtaining a recognition accuracy of 90%. Moreover, the results of the proposed method is validated using Wilcoxon signed rank test.

Current automatic speech recognition (ASR) works in off-line mode and needs prior knowledge of the stationary or quasi-stationary test conditions for expected word recognition accuracy. These requirements limit the application of ASR for real-world applications where test conditions are highly non-stationary and are not known a priori. This paper presents an innovative frame dynamic rapid adaptation and noise compensation technique for tracking highly non-stationary noises and its application for on-line ASR. The proposed algorithm is based on a soft computing model using Bayesian on-line inference for spectral change point detection (BOSCPD) in unknown nonstationary noises. BOSCPD is tested with the MCRA noise tracking technique for on-line rapid environmental change learning in different non-stationary noise scenarios. The test results show that the proposed BOSCPD technique reduces the delay in spectral change point detection significantly compared to the baseline MCRA and its derivatives. The proposed BOSCPD soft computing model is tested for joint additive and channel distortions compensation (JAC)-based on-line ASR in unknown test conditions using nonstationary noisy speech samples from the Aurora 2 speech database. The simulation results for the on-line AR show significant improvement in recognition accuracy compared to the baseline Aurora 2 distributed speech recognition (DSR) in batch-mode.Keywords On-line environment learning · Bayesian on-line inference for spectral change point detection · MCRA · On-line ASR · JAC compensation · Non-stationary noise tracking and estimate · Minimum search window · Frame dynamic · DSR · Highly non-stationary unknown test conditions · Real-world application · Smart phones and mobile hand-held devices · BOSCPD