EGYPT b ABSTRACT Automated medical image processing and analysis offers a powerful tool for medical diagnosis. In this work we tackle the problem of white blood cell shape analysis based on the morphological characteristics of their outer contour and nuclei. The paper presents a set of preprocessing and segmentation algorithms along with a set of features that are able to recognize and classify different categories of normal white blood cells. The system was tcstcd on gray level images obtained from a CCI) camera through a microscope and produced a correct classification rate close to 91 %.

In this paper, syllables are proposed to be used as acoustic units to improve the performance of automatic speech recognition (ASR) systems of Arabic spoken proverbs in noisy environments. To test our proposed approach, a speaker-independent HMM-based speech recognition system was designed using Hidden Markov Model Toolkit (HTK). A series of experiments on noisy speech has been carried out using an Arabic database that consists of fifty-nine Egyptian speakers. The obtained results show that the recognition rate using syllables outperformed the rate obtained using monophones and triphones by 20.88 % and 15.82 %, respectively. The use of syllables did not only improve the performance of the ASR process in noisy environments, but also it limited the complexity of the computation (and consequently the running time) of the recognition process. Also, we show in this paper that the integration of a pre-processing enhancement technique in the front-end of the syllable-based ASR engine leads to an improvement of the recognition rate by 20.88 % and 15.82 %, compared to the rates obtained using monophones and triphone-based ASR, respectively.

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Dysarthria is a motor speech disorder that is often associated with irregular phonation (e.g. vocal fry) and amplitude, in coordination of articulators, and restricted movement of articulators, among other problems. The aim of this study is to raise dysarthic speech recognition rate through producing intelligibilityenhanced speech using a procedure in which formants and energies are estimated from dysarthic speech and modified to more closely approximately desired normal targets. The modified parameters are taken to formant synthesizer to get final transformed speech, tested through perceptual tests to ensure quality and intelligibility. Then, we passed the modified dysarthric speech through an automatic speech recognition engine based on the HTK Hidden Markov Model Toolkit. Speech recognition tests results indicate that the applied conversion algorithm raises the recognition rate of the dysarthric speech from 28% to 71.4%.

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.