Speech recognition is a very important field that can be used in many applications such as controlling to protect area, banking, transaction over telephone network database access service, voice email, investigations, House controlling and management ... etc. Speech recognition systems can be used in two modes: to identify a particular person or to verify a person’s claimed identity. The family speaker recognition is a modern field in the speaker recognition. Many family speakers have similarity in the characteristics and hard to identify between them. Today, the scope of speech recognition is limited to speech collected from cooperative users in real world office environments and without adverse microphone or channel impairments.
Speech is the ability of communication or expression of thoughts among people in spoken words. Human communication via speech is essential since any impairment in this process may have serious social and occupational consequences. Malocclusion is a possible cause of speech impairment in addition to many other etiological factors like hearing loss, neurological disorders, physical disorders, and drug abuse. This article throws light upon the association between speech disorders and malocclusion.
Abstract
Objective: The purpose of this study is to investigate the family-centered care health services of family-provider partnership in Baghdad/ Iraq.
Methods: A descriptive cross-sectional study is conducted in Baghdad Province. A cluster samples of 440 clients who review family centered care for the purpose of health services. The instruments underlying the study phenomenon deals with client's socio-demographic characteristics and family centered care questionnaire which include (partnership related to decision-making team, supporting the family as the constant in the child’s life, family-to-family and peer support and supporting transition to adulthood). The relia
... Show MoreIn this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N
... Show Morewe conclude that Alaotaby in his Designation, with respect to defects in speech or speech pathology, he cited a number of terms function on speech defects in voice and accent like , Aphasia, Alokla, Alaay, Alramz, Alhasr, Alfadm,and Alaghop, and pointed to the sound stop as a result of an accident or a problem or the speech organ deny the will of the speech, which refers to the refrain defect in sound organic.
He also marked the disorders individual sound caused by the bug of sample and disability among individual like Alokla, node and aphasia - which hinders communication as well as other factors such as irregular sound product and not reporting to be into the future toward the Aljamjamah and whispering, and it can be said that he po
This paper presents a study of wavelet self-organizing maps (WSOM) for face recognition. The WSOM is a feed forward network that estimates optimized wavelet based for the discrete wavelet transform (DWT) on the basis of the distribution of the input data, where wavelet basis transforms are used as activation function.
Face recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.
This study aims to investigate the types of impoliteness strategies used in Putin's speech at the annexation ceremony. All of Putin's speeches were intentionally delivered to cause damage to the hearers' negative and positive faces. Culpeper's (2011) classifications of impoliteness, which consist of five strategies that are the opposite of politeness, were adopted. The data were collected from the President of Russia, providing a rich source for analysis. Qualitative and quantitative analyses were employed to achieve the study objectives. Qualitative analysis allowed for a detailed examination of the impoliteness strategies employed, while quantitative analysis provided a broader understanding of their frequency and distribution. Putin most
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