Isolated Word Recognition

الغاية من هذا البحث بناء نظام لتصنيف نطق الأرقام الانكليزية وذلك بالاعتماد على نماذج ماركوف المخفية في التصنيف وذلك بالاعتماد على طيف الإشارة في استخراج سمات الإشارات

Improving the extraction of audio features In audio-visual Arabic systems

The audio-visual speech recognition systems that rely on speech and movement of the lips of the speaker of the most important speech recognition systems. Many different techniques have developed in terms of the methods used in the feature extraction and classification methods. Research proposes the establishment of a system to identify isolated words based audio features extracted from videos pronunciations of words in Arabic in an environment free of noise, and then add the energy and Temporal derivative components in extracting features of the method Mel Frequency Cepstral Coefficient (MFCC) stage.

Improvement of Speech Recognition by Merging Two Features Extraction Algorithms

The speech recognition is one of the most modern technologies, which entered force in various fields of life, whether medical or security or industrial techniques. Accordingly, many related systems were developed, which differ from each otherin feature extraction methods and classification methods. In this research,three systems have been created for speech recognition.They differ from each other in the used methods during the stage of features extraction.While the first system used MFCC algorithm, the second system used LPCC algorithm, and the third system used PLP algorithm.All these three systems used HMM as classifier. At the first, the performance of the speechrecognitionprocesswas studied and evaluatedfor all the proposedsystems separately. After that, the combination algorithm was applied separately on eachpair of the studied system algorithmsin order to study the effect of using the combination algorithm onthe improvement of the speech recognition process. Twokinds of errors(simultaneous errors and dependent errors) were usedto evaluate the complementaryof each pair of the studied systems, and to study the effectiveness of the combination on improving the performance of speech recognition process. It can be seen from the results of the comparison that the best improvement ratio of speech recognition has been obtained in the case of collection MFCC and PLP algorithms with recognition ratio of 93.4%.