Sid-Ahmed Selouani

Speech steganography using wavelet and Fourier transforms

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.

Automatic birdsong recognition based on autoregressive time-delay neural networks

A template-based technique for automatic recognition of birdsong syllables is presented. This technique combines time delay neural networks (TDNNs) with an autoregressive (AR) version of the backpropagation algorithm in order to improve the accuracy of bird species identification. The proposed neural network structure (AR-TDNN) has the advantage of dealing with a pattern classification of syllable alphabet and also of capturing the temporal structure of birdsong. We choose to carry out trials on song patterns obtained from sixteen species living in New Brunswick province of Canada. The results show that the proposed AR-TDNN system achieves a highly recognition rate compared to the baseline backpropagation-based system

Alternative speech communication system for persons with severe speech disorders

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.

Experiments on automatic recognition of nonnative Arabic speech

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.

Evaluating the MSA West Point Speech Corpus

Compared to other major languages of the world, the Arabic language suffers from a dearth of research initiatives and research resources. As a result, Modern Standard Arabic (MSA) lacks reliable speech corpora for research in phonetics and related areas of linguistics. In recent years the Linguistic Data Consortium (LDC) published the first public MSA speech corpus designed for speech recognition experiments. That corpus was called West Point. Currently, we are using this corpus in our research experiments for speech recognition and other speech processing investigations. The aim of this paper is to evaluate the West Point Corpus from the MSA phonetic and linguistic point of view. The phonemes used and their numbers, the phoneme definitions, the labeling, and the scripts established by the West Point Corpus are included in the evaluation. Weaknesses, strengths, and discrepancies of the West Point Corpus regarding the linguistic rules and phonetic characteristics of MSA are also discussed in this paper.

RECOGNITION OF ARABIC PHONETIC FEATURES USING NEURAL NETWORKS AND KNOWLEDGE-BASED SYSTEM: A COMPARATIVE STUDY

In this paper, we are concerned with the automatic recognition of Arabic phonetic macro-classes and complex phonemes by multi-layer sub-neural-networks (SNN) and knowledge-based system (SARPH). Our interest goes to the particularities of the Arabic language such as geminate and emphatic consonants and the vowel duration. These particularities are unanimously considered as the main root of failure of Automatic Speech Recognition (ASR) systems dedicated to standard Arabic. The purely automatic method constituted by the SNNs is confronted to an approach based on the user phonetic knowledge expressed by SARPH rules. For the acoustical analysis of speech as well as for the segmentation task, auditory models have been used. The ability of systems has been tested in experiments using stimuli uttered by 6 native Algerian speakers. The results show that SNNs achieved well in pure identification while in the case of semantically relevant duration the knowledge-based system performs better.

Perceptual subspace speech enhancement using variance of the reconstruction error

In this paper, a new signal subspace-based approach for enhancing a speech signal degraded by environmental noise is presented. The Perceptual Karhunen-Loeve Transform (PKLT) method is improved here by including the Variance of the Reconstruction Error (VRE) criterion, in order to optimize the subspace decomposition model. The incorporation of the VRE in the PKLT (namely the PKLT-VRE hybrid method) yields a good tradeoff between the noise reduction and the speech distortion thanks to the combination of a perceptual criterion and the optimal determination of the noisy subspace dimension. In adverse conditions, the experimental tests, using objective quality measures, show that the proposed method provides a higher noise reduction and a lower signal distortion than the existing speech enhancement techniques.

Robustness of speech recognition using genetic algorithms and a Mel-cepstral subspace approach

The paper presents a method to compensate Mel-frequency cepstral coefficients (MFCCs) for a HMM-based speech recognition system evolving under telephone-channel degradations. The technique we propose is based on the combination of the Karhonen-Loeve transform (KLT) and genetic algorithms (GA). The idea consists of projecting the band-limited MFCCs onto a subspace generated by the genetically optimized KLT principal axes. Experiments show a clear improvement when the method is applied to the NTIMIT telephone speech database. Word recognition results obtained on the HTK toolkit platform using N-mixture triphone models and a bigram language model are presented and discussed.

Auditory-based acoustic distinctive features and spectral cues for robust automatic speech recognition in Low-SNR car environments

In this paper, a multi-stream paradigm is proposed to improve the performance of automatic speech recognition (ASR) systems in the presence of highly interfering car noise. It was found that combining the classical MFCCs with some auditory-based acoustic distinctive cues and the main formant frequencies of a speech signal using a multi-stream paradigm leads to an improvement in the recognition performance in noisy car environments.

A noise-robust front-end for distributed speech recognition in mobile communications

This paper investigates a new front-end processing that aims at improving the performance of speech recognition in noisy mobile environments. This approach combines features based on conventional Mel-cepstral Coefficients (MFCCs), Line Spectral Frequencies (LSFs) and formant-like (FL) features to constitute robust multivariate feature vectors. The resulting front-end constitutes an alternative to the DSR-XAFE (XAFE: eXtended Audio Front-End) available in GSM mobile communications. Our results showed that for highly noisy speech, using the paradigm that combines these spectral cues leads to a significant improvement in recognition accuracy on the Aurora 2 task.