Invited Speakers
![](data:image/jpeg;base64,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)
Prof. Mohsen A. A. Rashwan
Professor in the Department of Electronics and Electrical Communications, Faculty of Engineering, Cairo University, Cairo, Egypt
Managing Director of RDI Corporation
Speech title: New Trends in Artificial Intelligence and Human Language Technologies
Last year I have reviewed the new trends in Human language Technologies (HLT) as per December 2018 advances. Year 2019 has witnessed so many more advances in this field. My talk this year will also start by reviewing the new advances in the Artificial Intelligence (AI) field as it includes the big picture of the field. A review of the main factors affecting the advances of AI field will be given. Among these factors is the data and the advances in dealing with the data. So, I will give more concern for data, data annotation and the tools that are needed to make good data annotations. New trends in algorithms that will include supervised, unsupervised and reinforcement models of training will be reviewed. New advances in cognitive sciences that contribute in developing new era of intelligent machines that can learn adaptively and continuously will be introduced.
Then I will focus on the most evolving trends and applications within the field of Human Language Technologies that will include: Chatbots, machine translation, data and text mining, automatic speech recognition, OCR systems and more. I will end up with the main challenges and opportunities that facing the advance of the Arabic language technologies.
Bio: Prof. Rashwan received the B.Sc. and M.Sc. degrees in electronics and electrical communications from the Faculty of Engineering, Cairo University, Cairo, Egypt, another M.Sc. degree in systems and computer engineering from Carleton University, Ottawa, ON, Canada, and the Ph.D. degree in electronics and electrical communications from Queen’s University, Kingston, ON, Canada. He currently serves as a Professor in the Department of Electronics and Electrical Communications, Faculty of Engineering, Cairo University, Cairo, Egypt, and as the Managing Director of RDI Corporation (www.RDI-Eg.ai) that he cofounded in 1993. Over the past 25 years, he has been pursuing and leading R&D projects that focus on basic and applied research as well as building commercial products of Arabic NLP, digital speech processing, image processing, OCR, and e-learning. Among other several national and international mega projects, he has served as a Senior Scientist in the EC’s FP7 projects on Arabic HLT NEMLAR and MEDAR, and as a Co-PI in the Egyptian Data Mining and Computer Modeling Center of Excellence (DMCM-CoE). ALTEC (an NGO to serve the Arabic language Technologies specially the Language resources: www.ALTEC-Center.org).
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)
Prof. Wafaa Kamel Fayed
Full time professor in Department of Arabic Language, Faculty of Arts, Cairo University, Cairo, Egypt
Speech title: Lexicon and automatic processing systems
My Speech this year will deal with the lexicon and its relation with the various systems that deal with the automatic processing of natural languages, It also will shed a light on how useful using computer in building lexicons and the beginnings of using computer in Arabic language studies.
The speech will cover the following points:
- Comparison of paper and computerized lexicons.
- The potential that technology can give to lexicographers.
- The concept of automatic processing of natural languages, and its aim.
- Arabic language and the challenges of automatic processing.
- The lexicon and its role in automatic processing systems.
- A comparison between the computational studies of the lexicon and the traditional linguistic methodologies.
- Conclusion.
Bio: She won the Cairo University Awards: Encouragement 2004, Appreciation 2013, and Excellence 2016. Corresponding member of the Arabic Language Academy in Damascus from 2002. The first woman to be elected to the Arabic Language Academy in Cairo.Board member of the International Association of Arabic Translators (ATI Collection) Atinternational.org. Former language consultant in Microsoft, Cairo Language Innovation Center . The author of the first thesis that used computers in the study of the Arabic language in 1974. She published 74 papers in foreign, Arab and local scientific journals and conferences. She supervised 60 PhD and MA theses and participated in the discussion of 32 Theses.
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umgUT5yvPYCmysoR3Ciod+1KHiB8XOi+k7zj9waN5uTIBRaYqT5jiz2xioY6c8VHXbrrqeHA0joU6PtLspSlXGWztDbJ7uFSiAeKqZbbBVFxByVqXXKa3RgSDY8LbXUzQjMGA+yptYbEcpSz5WB27mubDxg9L5WntTTtWW1luOtl1DrS2V4UHEr4I9sHFdEvVqBr6xQVS168tuoJS0/zG1DegnHOEhRxVOXWqS71AuV5sVytrUWY20poPp+JCuc5A981WUbpbRWh44yotfLS3q1SRvGQR+a3f0L6vM9VOkWh+oO/ff9Oobs+tx5mSl5Aw28of8AUAOa2dpi4u27XeqZbTq0CbKgTELScBKlpyvHzykVWN4Jb2vRvVrXvQ6+yFRYOvrbINmS8rCPvzCSttQJ9wMCrEtNSHpBfKwUymIgRKJHO5o4rqr5nVVE2Ts4LzQ2nNuuj6dw3BK6euhGt1a86V6Vv6HUyXP4emPMdGBh5v4SSK3EFqxgqyUnCjj1qtj7PTVapmlNXaVffcWq2PsyYbSlcIQ5wvj/ALqsdW8oqyk4BA/prPSN0PIVihVrVuPP0oJbiwAM8HPpQalqz39B6ViST3NN7kbIxysipXv6D0rzer3+lY19RollvV7/AErEq55IzX1F3FAKIOe1Nuf4TjWeUP5u3IB/aglq3KJoLcn3I/SirvKyQo9qYc1zingQAlNTiQo570GVkk7TxRdR3Enn9axDgTkcftT2lx3TWsDZGw4B+I80C44Sr4TxigVKyCoUClajnJ7K9qGhyGtqNBxY7K+lehzOSrJP5UV3q9/pQanACdx5pwbBNnco+XcJIGcY9qPQpQj23Vb53BTGjLk4lQ/6WVH/AGpBSoK3AdiOaVrRGcuSNRWdJAXd9J3CJHPr5jjCsVZWYA3Ngd3P7KvrgZICB32XL79kVIut3uni01dJnKg27UHXdwXm6JXsJZaSvKd39IyfSpgeJLxl+DxEDVGktM6Q1P1R1BpWKr/iGVoaOSzCe4T/AJiSpQSMnvzWsvsmegzl68O/Xy26gM+3s3nr1quDKRDeUw9ujSlNH4xzkcUHrf7PTSehbVfYmmrfd2I98eeVf1QpjiVT0rVn/MH+s5HrVbVVEb7w/PGSuxWagnk6dia0jOB324Cg/wBJvE/0guWsrqiy2XUWjb5ZpJS4wqcH0rXu4+NCik9xxmunbo7qCPrrw9xdQW99b4VbcLW4cq3gfFmufHpX4EXhrKILVp9+PHFw8yQz5eCUnkFRA5roy6RaBPTXoc9ZClLTm1xx9oDhKVegHp2qFWiGIlwWlgbPBE2OTGoELmu8XerLpauuFhiQYSLopcl1UqO8rhCUZ2/rmo13i3aq1vp3XGptea2vNiudqZSNDdOLfIkw1XNW/Cw4tojYgJzgjv61MHxTaWjak61XKZb21IEd9HlPbjlJB5wa2BE6W3bVWnrfdnlIJjoCRKW2AQUjGArHA5qDZrnDC922cq26gt81eG4O2FQfYbN1ksk+7G0XTWVnmybk45bGm75IkR2kqVw0fMUSoAY5NTf6X6D6oaleiS9VRoKjFjpXJmMnJWvHKlfOrPNIeE+EFuXa+SIUdh3DiUtoCyc9ju+dbVf0HpDTFodiQWmdrqFBbyj8Sjj09qg32YSOydt0LXaxTRgc+crnR8RWn5PS3qdofqrbFLbd0/qeI/MWyjH8tCwVj8incP2qzDTDzMy/ailt7TDuLCJkDyzx5MhCXUj58Lx+laB8YmloV007qaGlttZbiLdY98IBIA+eQKcPhvvq730w0ff3lqkPyNBsNTXt3/1bJKFD5YASMVpuma716J0ZOccLkH2i2kUd8ZMwYDleF9nICq+dRHkK5Rp6KgAnjBkHPH6Va0BgYJzyeapq+zp1nbLfq7UlknrU1I1FammobiuElaHN2B88mrk0EbRk4Pru4NPyn3krHs1Fu/KEPf8AQV5QLjih+EisWX0ht1b3O1Pw445ptvCM7IfI9x+9e5B7HNJzEkPoLmCMrIAoQv7SQCAPmKSQ4pYLAhXXFpXgHAx7UTdUCSpRyqvVvZUScE/niij7iTkn/WmyCCnRwh0rTs3AkHPoqgS4SSQaJKe2oIBwM8cUAJKgOCMZ+VEglYPJHY/6UEt7Kicn9KTfPH93+lBKe+I8n9hT42CYO5SoZKhlIVx7EViJBGcEcn2pK835qr7zfmr96NElX7yfcf8AjWCnAo5J5/Kk3zfmr96+835q/egglVDoSMDJ/JNLmnJ4hXy2y1EJQzNb3ZHfPH+hpopdTjknv60J5wSpojkpUVJA9x/6VIpZCyraR2I/9TM4aWHP+lRj8OWhYnQ3XniG6WkeRBX1yu2obayTkfdb4oSUEH1AVvFTHvumbfPirj/d2El4AKK8Hd8xntWhOp0ZyL1H07rmPGUU6isH3eZKQrhT8ZYwFj1ODx7CnA71FZQhpT8oFDQ+DcrBHy/TNQ72yKnu5dn7+4+CutdDzPrrEA3lntPyPCfyNPaZ0LBQ+2xHTMkqABDYUtaj2ANOKZIfkaBv6XWFMPtRSGkLb/pIqFup9f6l1Vq2xWvTLgWtmV5q5T7fmNI29vgHJoPqD1b6saR0jqQautLLj5GWJtgbXtcaA4JbPKTiqKoqNbSOy3Zt5a5hJGo4VTnWPTt8vOvdSybbIhxGLSpa5BkJwpZ78U8vDn1i09q/TN90PIcjovFvkLbXFWvGSD+IZ96rQ6ydWutOrtf6gXp2VKsdjeuX82KbeFvSRntk8il3pXoTVOk7m5rG4zpUS6XBSXXW21kZSOwI9+9Z+L2SbcrXOYwAasYCuHGqnLFDXa3S2pABSwlQyBUYurPUFNliuJW/lQGUhKvekZ/qNBuluanrluMyYjQS8w458SlDucfOog9Xr/KurjSWXXlpccSQM+57Uite47k7KQ2OlfHlqZXVG6SNV2mVKe585tSGk4HPpz7jmm34QrquB0xuml5CktuWjWV4aQXG8kMqcBCQf3p/3PTjzenrY86hSUObSveM5I//AJSX05sdv0/piSwykJn3fVl1kkE8pbUoFJ/LvWh6Pkc6R47FcZ+0mmdJTxEDcFSu6MdRf+D9UQtQR5Tkc2u8MqZKVYB2L+LI9QRXTDpe+R9Radsl+jOF2PeLa1IaXnPC05/1zXKNo62xrvKbZLpSyyF5dRwFOH8X1NdPnRm2rsnSfp/a3yovQtKxUuFa8nO3I7/IitVURGN/yuUSwPgAceFtZbgPHbHyourB3AfhUORQG9JJ+Ij8lViXcEgEkUwBgKPkO3Q6UhICUjAFfFOT3UPyNFDIIJGR+orz7yfcf+NBBZuEpWQCe3rRN5zGQTzXrz/c5Gcd8UmPv7sknmm3NJKcDwAs1ujaefXmian8Hg8fOgVvthJBJ/IGk1cgEkg8H3poua12CnAHOGyVXJBCiAcDHpg0GXyTkk/sKSVvlSick/oKw84/P9qkjcJgggpY84/P9q+84/P9qRFSCD3HyyKKvz3W1NpQkKB/EaSXgIAJy+cfn+1fecfn+1NU3loFIUlSMqIKigkcUVlalt8JtbkqZGZQgnK3X0tgD/7qNuXnYIYT4S8rHHv6ivfPKFJXuSFjIGQO1aYk9UtFR3cStY6bhoT+MP31pKv2BxURvEB9oh0U6IQJEWJfIms9XrYUqBYLTLC2zxwXHhwAcVIZTVL3jS1NSMc8bKxu6MNXuyKgOLy/brg3MgOIVkJUkEONq9AFJP71GDqBBkRUveR5gbQlRQpteQSefT9q5s+o/wBp74nusWs7WxpXUTuhrJCvTDv/AA9plwNBbDboWpLzvdeUpwQeMV0Z6D6tWLrX0v0zq62PNyjcbe0qUpoAhEtIHmtqx2wc1B6soaymp2SvHGF077OJDT64ied1rHQHWDSOhdWtuarlMWpxA8sS5kgI+JR+fI7Vu7qP4ovD1MtU6HO1xbZT0mIpCzAIfwVDkfn/AKVpVrphpHVnUxmfeoMObGKQswZSAttJT2ykivdedFumuknnr5bdHWhtRkLdfQi0Bxr4vZPYVTUDo5G+7YLo8sUEle0vJBVdvVHqd4ebEy6/pSPdtRXRyYovyI1tz5YJz+ZqF+suu191M6u26D0TqCQ428EplSo5Zawe+cgGrGepI0RdAYVu0ZcozZXuQqzWVMdtTh75UBk1puL01ejylPsWmTEYkgKWJA+P9xTdUyna4kLQziFjOThRq6YWvXd71Pao+qkMxESHVfeYMdzcAkdvi96fGq7DDd1Ui3obwwxOCFZPoDWxf4U5p3UX8ZjIw3CbytKlZyo96YT01Uq7Sbk8oIKpKnXPh5+VUFSx0gwEije0ZJOyH6qy4cK2wbVE8sLbSny9v5YrQt3uTVjgNubwJC20s29kfiLqu5+dK2qL+3crtJnSntkO3AlRUr0HdVMvp9orVnXHqFb37bBltaWs0jLMss4Q7g/Ev9fStl0ZaJXAuIXM+t7lC2oa0/db+ql30ctibdbrdJvSosViQ6hMh5842gqBWs54AArpT0Tf7PetL2Sfp2422/2k2tgMzbNMS63tSgDuCQCMcj0rnO6jW17TulbrbmUbmrdZXyoLR6pbIOfeqHem/i1659FdRXKZ046n6v0uW7i6WYFvvDn3Yo3khJaUSnHyxWqr4Q14b3C5NLKJuV+hx95Cud4Vk+iQMfKglPfEeT+wrkX6Kfbn+InS8iLH6tWLTvUW0JCUlxMNNvmbfcuI4Ufzq0/RH20nhj1FFiL1ND1bpKVJAD6XrcJDbavX4k90/Wqx0MgPCjhhxsrk1PnJ5r4vnaTuPeol9L/GZ4cusRab0N1W0vNnPpGy0z5whygf+xzGaksJzLyE+VIQpJQFbkELBB9lDIxTbmObyERDgeEqOSCUn4v1pPdfOCM/piirktO3tt//ACBX1FJrsknISrj04FNOeG8ow04Q0h/k/F6elEfP+fr6mir8hKQQtfPypMVLIUdpTj0yM1EkIznspzInlowlVUg579x6poMyyQptsKcf3DCUAcCkBU1SThRWScFAQng+m39ap18fXj01h0gvr2humcmHAuFtbQL5dXYyXiVq7oST2I9xVpTU8tS/S3lQw0vdsrVeo3W7p90siPzdaalg2vyE5TAS8lUlR+aPQGq2+qH2odoimVG6eWSGoNIUP4xeJHCcdleWeDVE3UjrLq/X90F61JfrhdZ8/a46+/KUo7SOAOeAPatUy7s7dJLbTrri2AnBRu7n5+4rXUvTEeAZMZ7qQ2jDhuVZvrb7RTqvfytZ15LjxnhtRBsKBFZB9SAPX51GzVPik1lPWkP6nv8AJlPjLpk3RxzKT24ziohzVpD+QMNNJwQPQDtSGJBddW6XSpCAfL38nFXkVnoodsbIy2GP2rcuqOtmprg82hNxlHfnzCXCBj96j5qbVN31Dc2Ya31yXHHU9/iCce9Epk9Tr6mmyXXSTt28UbssaNZ1rXcG1GfJWTlRzhJ7Y9qnNpWBw0AYHlRnuLTtwt86IiwLZGMZUNlc15O56a26UuZPoPYelW+fZWaj1DqfqNrvorbr062qfpWTedKafnP/AMtyTFUC+ltX9ykkYSPUZqnHSiZLbSn0upkfEShKzkY/KpbeE/qy50K8RvSfq84kRY+ltWRV3lDatqFW55flPkj1AC8kfKp9RaaW705glHI/NOUtzqbbKJoT7gV0GW3Xtq0vq0fxliTBnodU3NiyEALZdBwdyTz3qa+nGNK62jRTMeS7CfYCnUNkbSn/AGNG/Et0A0R1V0/A6xaPtzF4TOgtz25engEyZcdxG4LTjhRweAe9V5WfVl6tzdwhaH1C7PRbcidanUlNyiEd0PRjhXp3TxXJ7p0PcLR7o264/A3wPrsul27rOhvBxK7TIBvnhTm1/oLpmiOG4UGE2whvB3tjdx65qAnVPUGjtJRrglr7sopQUxQhQyDTA1p1k6iCK8h1pS3EpIRvcO4fJQqBGuL/AKovMp528vhpa1FSQt8kYPsB3NY+up5i8t0EHxhbihrqaSnDvUDh8rzqR1GSDiCklyQ8S4EjORmtLT9aBpC233EodeGAykZWsnsBinTYem+t9c3FKbBZpcrera7c7khTEZCR6pJ74qcPQzwo6SsF0bvWtC3qu+Id3Nx0kKYaV6AJ+XvV5090VWXSpaXghqyfU/WVHaYCI34cfj+VEvpX4WuovXKbEmXG2TrBoovBTpW0USJQB5T7hP8ArVsOnugkDpnphqHYIP3RliLsBQMEccg1OPpV0zmKjwUMRWYNvVjym0thCgn2BHpT56+6Ttmj+n8q5uFKZDbfw7FbtxI9q7ZDYaG0UYYwcBcKreq57xWYc8O37Fc6Hi9vbuh9A66vktQR5Gn3ktqJwFbwUgfnkiuXmUh5KlqyAt1wrUs9zu57/rV5n2rnVtxjRFl0XFy1cNW3tJeG3GyIzycj5mqNYyFvIZ2O+Y1j4VKHOK5tXMM1c7T5VuwkDcYTghb1xUvZJeZ4BJ7AU9rO84ppbqDtJRlS08HNNy2xkob2LGd5wofKnFDzFd+7JR/IfGELUex9easKOnzjKcDwAnXFuk6P93fjypLLjC9zbzLxQtKh6hScHNTT6Q+PjxPdHERo2lOqN2dtbYPl2rUDn36IjHf+WvPeoMtJKiWCPhbJ7H1o4lK/LCE/BtyNyDgkH3qXNb4Hu4SxPgLoe6LfbTLWI1t626CS825tS7qjRigV7j3Wpg8Y/KrbumXir6HdYLdGl6E6i6eusl1pJctMicliY2tXYLbVg5Hyrh+aSkMkbnPMZ/5YKuE/MfOnPZdQXGzTI8y1XGZbZaMEy4Txac3D/qTg5qrqrE2VnsG6fGk7ru6kXRKUgvpQkYGHEBXxfMemKTBd8/gSAn0J5rld6UfaNdf+nDtpts/VbmqtO2lsOSLVeo6ZD78dJ+NCXFcg4ro26KdWrF1x6bac6kaTDKbbfooU9G8zcWHx/wAxo/NJrIXKhqKIe7hSGknYJI8S/WVfR3pdfdRx5CE3uagQ9NtLAURLc4Dm31CQa5X/ABAamvF9Xe5tzcduV1kSFuzpks+YVrUdxP7+lWaePfrcjWHVaZo62zCqz6ChqbIQseWuSr8Sj7kHt7VVB1EdkSbbMYjPB55bClvvuDOQRnHNdBsVBFHTCRw3Iyq+HB93Zaqt9xfkRYbro+IRsu7R6gcUoafe+9LkLd/EglTYHH701rCp5GnW5alg5SpCyR6g80uWdao7SHynamRnOFdkJ7/qa0cJ14CV/UPJ2SrcXkNRX3HD/wDMDDavn60xJc0MRQtLmS4SkI7nNH9U3NTcePHQRuT8YGORu9DTds1sm36YFtNeZFhKSuQkLwo574FGXN9TT3Tbn6jlKtqiJZt0ifLdS084o/dmFJ+IH3zRu2hyQ6VS0qdKRlKlc4/WsdUOfeZsaGEeW0ypKW0pb2HAHY+9Oe0QFxktKdbdfacI3BCOcfnUiFhykOPZPLTzbiUeZHUTvTgteZyPnink7eJEWA81cIbaozqFJLjjQCwn2H+tITce2xI4lRJYhvFPwsO4KwfemhdbtKlupZlSnHo5/Cndg1Zx5Dg4ItDXR47rrg+wn8Xtz6wdLtYeHzWUO53FXSR1peldVS0l5DtvdJP3R0n+pBGAT6VbJ1V8JPTDqDc7ZqpmzpsWsY4V5WpdPn7s+QvulZTjePcKzVRv+Gj0vbpPSjxM377iyuVb+plnQH3GwVeUIu4pJ9viPFdIfXViwdI4Nn1ndZ0a06PnT2GXpspZ2R1u8IC1HgBSiBmrOSrYyZre7gPxOAue100sdW8A7An9VS91O8C2r0iQ9Zr9EuTHl4SLhASHAPYGoc3LwQa9jyxIMBMoNu5WTbELUD64FdH6JVu1BD/y8luUHUZDaSFI5/tI7+lNSbZoFjQp2alqPvJI8/k80w6Gn9T3RtP4JymrKiCA6ZTn5VG2nfC1cFiM1dZmpWkt8KtqWvLQPmABUltD+Ha22Z9txmFOUcgKcktkq/OrBprFlekNuhcchYHmFCATj05ptz7rGs8kN+ck29xBK5CVDLdS2ywjZgA+FUV0DrlD/lyfxKZFr0Wu3w0BN4LCmU/BGztOMVV/43utN30YzJiXSe6iywEAvFxe4Kz+HHuc8Y+dS+68+Jvo50btEm7606iWO2RUpyIyrmhUsk9ihAOSPzqnjqGq8+MnVli6h26JqGH4YdFyVy7rqq+WowlaiuzZ/lRIgVhS2E/iUscHtVTeJJGUD3eFW2WxU7bgxzcgj6qgn7UPUK7rrHo/EWpbU6TpSTc58davjb+8OfyEq+WzPFV92WAsxGFLc2EZDiceufSpd/aRypt48U98LzYYjJ07bmoMRA4aYCDtSn24AqOEKKhDDKFJwdic1yeniknkLguytJIBWbAQztSlRXj+rNL0dL0xBZCTsI4KTg/vSWW22vhQklXfnmlG1qe+9JCVbM4zxir2ljcCMo0bZU7CkJYdBSMDKlnPB+dLexbagsqBZWn4cD1/OhLjbBJYWUKWuShIKNqe9J8aS47b1xckSIbw87I5wTVoAAglBtC/vG1JGHUjcO/GK9bSUbkuK+JKzgqJzj9q9aKwtDxWBtIGCPSh5qU7wtJOVJycHjNOFpaAUoPIR6HJDjxSoJLjTKtjg74Ppn2q+P7J/q7brd0j19pTUMtxKLBrBpy2MtvbAhuS2pSv3Ug1QFBd2zZBJP8ALhFQPzqW/hC6hv6XT1FYDym0ypNsUP5+wEgSB2/UVmOpqdklM0t5T8D3akbn3iZrC+ao1TNedUq53N599x1ZOQtZNav1NLeejzWGgkF1tzbhPOMU+2Sm36fMNaClcwb3BnkJPIGfkK17NkNLalOrB81DeyNzxjHOR61rqWFkFOG/QKFS+owe7halsaHVWVuJyUNS3QpOPnToceRGbSynGCyOCOR60m6TZdehXBxZA8ue9tyO3NAXd8oQ+4VYWgJwePX3pTgI4gWp7gJm3uUuRNeWVbgpQyNox+lOXTc1CIyYjC2WHw6VPLSshxQJ4Bx6UzlgPvuJ+JRyTx70rwoKiw26h4xn1SMOFPCto7c1EiJMuSkp3T2VSb/HauCy3lpKkqLeOMcGna5OXaNhhykuoU2MqIBTn1FNr+IrleQJq0y348PamStHxYHYZ9aIRXHH2XGlE+WhRIA7j3q1i23RncJem3lXltPKHnOPlW4/lXzcNb2ybI3BtSUFo5wP+oUFCjM+SnKN6WnOQo9gadLn82FHjpH8pp1aMJ74IyOasGEOAARjJK7Fv8MpDQvop4p07cMP9RLUcdspMACr/wDxhWDS/UrwY9S9GauucKC1ftIyYdukyVhLn8QAJj+SO5cC0pxjniqEv8MO353Q7xLJUrJX1CtLZ/JMLn/apS+NzrEm6eKfwddB5NxeascHqXfLvqq3JeLSFpaZUICXP70lavwng0ctO6prG45Zg/gAFzmtkYyul1dyR+art8BXjplsMw+letNUrtvVPRpkWz+F6kjuuRrx93CkIUwpOSlz4ElQNCdT+qXjSvdxvU22dS4bK35UgtQ3rU2WIQxlpIbPdB4G481oXop4TOq2h/FDqnrrc2WI1pOuLy7Dts2OWpDrz7zn/LTjCWwFcYrc96vEx7xIaU6eQtRt29eqH0w7swhBMdqQ8k+V5yz2JI59zitbHRGdrnPIyRlVclVUUsLnxEYG+6jHb/FX9ozDRcol4tOlbC9bE5dmG3plKfTuwhTae2F98jtSnceonis61a20X0PjdQ03bqPrZ9pqTCssv+H2q2h0bvLdU38S3PKC1EA8EVbN1m6aJ0PpLobH1EIjcm4x/wCA6wvjUQBKFPKUlt35bVlHNRCsfT9jwudS7r1J85ubcLcXRp1b0VCkR7upJR97S4rsVIUQfTmk01LA6nJb94A/jhRLdc57iwnG/f8AHwtWa+8Afhy6Zant6uoaeoXXjW9lZakXJ3Ul8+4WZp/OfhZSdz7QVn4V5BxUpOr2tZGtOj2l7Qm36fszOggmGxYNNQRBiMQHB/LebYTwBlOwn1rYRg2O6aAtevNe3Bh3VF6khUDSTNwE+4z5aj8CBj8LfOTjjmhL10QiWnQGt7rfpDCOo+rNNb7Vp2K7huOyyd6GtvqoZJJ96pKyhfV2uVxOSeP/AIrWGtFLXtbjHlcUXj1jsTPFLfm1JcdQ1ZLcA4Tg5CDx+nao0+WA+lBGAMAAewqafjc0jNtvX663i4JCP44244Gs5LSGMJ2k1DhDXmJckYO4AlsZ9M8VziC3y0o9wXSIZmSx6hwgmW0rU5uBUUEbaU2Ial5eBSylKTlwjvSU0paEKUDhSjycU6EpQiLHJKj5iVdz61Mpgx+D5Tw3CbsjU7jbgtVoZdlz3DhTy1FKG0n1z60f07ZjCTNK5jkt+YrdKddVnCv7R7AUtOR4YgMISw03IcCy64hACsemDSC/M/ga4AkuEpnu7EJ3fER+dSXREDKSXgFLGwnKVn8CyBtNePOJaipO4lQB7mhktpS8pHxBKlkgZ5xSTPWUNO7lgJRnaDRzHEefCUNwkpqe+l66OhO4tQAEK2+5ptsahuUJx/7nJkxQ6oFz7q+UZx2zjv3P715ElrejXl0ubklxDXAx8OOabrE1hsLRuyQsk7huPP5/lWRulQ2VmlORPAKsQurwLcnCgoocUCkq7KxyB8s1q2Y+kp8uQSh0byEDj/tphP8AVi3LKG5ZkNzVr4CU4Rg9ifc0lXnVCnVNTVSG3ioJSSyngI9P1rZmojaxNp26RWldivTzisqTd3EpIOByab9zB8hTaidz2Cr/AGr3Rc5CrZdrenKibl5pV7Bf/wDjRq7hn72UAq2tpA4NJB9aHbsj7JqQ1IjzkMnH8xs4Kjzu9KcAT/mUH0UpJTj2xTUcWk3Nl1WShlznBp1laSqO6n8ARx+Q7U1C0l6JKkRCGpRBzsIKU8+lGYgLbjrQxsKu2PSiThUFNOo/CvaQcf8AlSmC2h8kZAUARgelT43Z28I8ZSrbD5olMLOQtnKR25HanFCSFw5BVncy2lwEH+sA014a/LktljG7eEjcnPfvTtgtqSp9heQl1SkrGMcAVZRgtARjAOV15f4YfVMRnp/4k9LKdT9/lawts1LR7+X92KMj9RVt3iW8I+i+qHX/AKc9YLtPdtMvp46qTPSx8H3lKTltJc/pwvB+Y4qiH/DNWDUzuv8ArbfmFbtJItMKNcgEfgk7ypBz+Wa6K/FRr6Jp96Xb231NpWx5t1cSrhtpHOP1q5o4y6v9uxLR+i5jdIDVXd8RGfd+pyFCDxOdVtO6ItVzuUdEaTOjB5QecAAS3jCin3IJBpqdNvB1bdS9BYWvNVCbD11qyfG1Eq8JVtlR1MrDsVoHuAAkZHqFVXzatUv+MrxPW7pJph1UrQOh7m3K6jX5hwllLba9yIoUP6lkDPy4rpRv9mix+mwtsBBDUe0IZYZb42hKQlKR+gFW1wkbQRNZHzyf2CgXNkEcboQc9ioF9Q7/AGbxCeHC9X2ytPGXYnfPQidHAWmRHWQrA9BvbJqD/XiGrqJ0Mm3eKkLfkWBQeW0rKg9swrHsoKBre3RLVEnRfXjWPQbUTYjWzVtimybK3IIDSnnHS4UAe+3dWvoVqGmb7rbpldG/8lKuDzlpZWMpCHSSAP3pFC4APaPkfB5USzsZDp8FVk9HNddSUdPBrmxJdla26eOCFP8AvifNd+4o4S8gHgFI4URya3V056i9ZuqF+g3BuU/c7zIlhtpchzDbfmZBSR2wQTT26KWa19OOrmqdI3mGhVtvxd2tPD4XY7qtqk5PtnNZWy4q6B9RNR6eiNxlvNX7ZY1KbCiWnDlC8Hvwrv8AKmGSmnqdGdv9/JXlzihDdTW+7lUA/afdP7loHrm7a71IbfuU3S6Jbv3d3KG3JDhK0j/xqsRG7yFITgkKKeR6DtVwn2yl3jTfEnZYsEpccjdM7MqapIGS64la17vnmqc3HnGvhSAla3CpYxnvWUvzIv64ubwthY3PNrYXc4CMW2xXm8SGYVrt0mY86sABDZ2gE8qKuwApxausFy0TOt1rva4y1Px0rZXFcChhXzHr8q2fovWep4+lv4JbYDD9vaddLaocNP3ovLIKlKc7+WOxHzrV/V+UlmHpW0Tt8nWaFl+5uNthtpoLUSloJHB2px8XrmspLK2lkx2KtYpGyN27ImndIeUE5ISlKEADHwj/ANa19qmWm6a1sttbBUxbWkqWhPooc806bJOeSh16QAjyUqLgx7UxNJb7tqm+3YFK1tObGiU8AE81LkqGl0bG8nBSTuVtyQeG5SUkJcQOf+qmXqpa2IbzjZIUW8k1sCQz/wDC0IPPkPArwfQ1rDXExLNrkOqOAhsg5HoO1SLs5sdKSE8w7BM60PtM6bfdWT5si5HzCT3x2xWvd4ffkOAkJKxjB/OlJm450602lWFPSFLA/wBKbiJKY48rB8zJUvPz7Vz6aQStCNuWndSwnWmA+lb7kZAQQAgY7UxZKG4ZdSpG2MeSjOMflT+mzwpAjII3DlSiKYWoP5qUspVkLA3lPyrbTloOyCdHTKS795vrLoCm9oWglIzj0p0SUKdU+9znuP8Aemr03eQk3VeAXw0lAUf7R2/WnxLCkxDsAC3M7jj0NS6Y/wCBH2TIebQlmY5zuO0tk+/rTji7HrY04CctgJ49/Wm3c8tstoHClL5pftW3+HuxxnenCyM+9CA5kRJdSk/dWiSPKYCgARzk0uZa+6wpCW0lCmyhaT33J9abUZanGnUqUcEJJHzpxRtyrasp58l47hjP4qlxsLZCjCVRHKHW3WiEkpbWjGP1pxvOLjv71ElTiAtJCBjBFIbaY5iRnMq3o3pV8XGD2pZuiGkxbXKySDHU2/g/1J/DVkJBsgCMLrK/wy9+bt2nPFDblltqOi52qT5jrgCiQyoHHypW+1h8S+qYrlw0D06mOPa117eVw7e9EVuVHYztKiB2HNVe/Yba61vbutetdM2CY7G0/frC4u+soH4nGmiUGrJIHROdqvxMz9U65ZfloYuROn3JDW5pDRPf8+9bK0QMMZmJ4A/j9liDJHSXioe8ZxuP+luPwUdGIXhi6V6FfLTJ1bqea3P1hcV/G7MeXgnzCe+Mmr9LKYt+sFtdQQpmayhaRngZ5NU+a1v0T/ja1WCKltqJY2G2Gm2x8HbnNWR9C9VN3fS38MLwMqzOAKQPxFo/hx+VIuVO99O1/dZJ9G99KZXH3O3VE32iWvb5048YWjNU6YjOhPTO6WqTezGBO6I+ohZWB/SATUkutj0afO0h1Isyw5HuLLLjsls5SpC0g549MGt++LbpXpSfedQ66k6ej3G+3qymBc1yRlt2MQUjI7ZSDkH0qDPSDUcm+6QvXSDUCNt20Mnyra4tzPnQ8/yilXqQnAqvt7vQkDndv0R00jXDA+US6paaelXTT2sLXuMhl5DnnNKxxjBSfcfKtCeMW5Itj3SbqHESoP3TzYd7U2Pi8+IgLZUT6ZTkfPipd2xt1q1ybDcf5sm2Sx5CVDktLPH7VoPxU6QduvSe9toj7ntMXePLYO38Df4XSPzSRUi907TG2RnKt4axr3tDtwRj8VzDeODVl31h1yl3y5yXH5c3TFsUjer8LJSoJT+gqFckKLx2KJUCEqKh6CpK+JaZ5/WS8h3c4qPZYaR8XbAVx9ajuFJ82S8ocBIKR88c1z+4TGok28re0kDoqQN+i2hohy5Q9MTJ8KZJDzZmKS01b9zKWwgBfnun+kj0FaWvN0g6h1e0/CUp9uAz/m30PKWgq9Akq5wKPKl3VVskwUT5qLe6y8gw25BS3h3hZwPU4olp+0RLRbUoZZ8tBfShSs5UofMmqSWmmlqAeyciaY85Rq6uIt1kucwhSS7GOFZ43HtTT6UtpQxLeXuBkOKK8nuqlfXcxxnTbcf4VCbJASnbztSa+0TEMdlCEEBLige/oaXG5rro0dglLZ7S8R5sdz4g83uR+YrS3UMpds0prJ5aAUM+tbheUlp8BZBTsISABWitcyv8rOaURws4/L0qwvrmtgIT7eFqCM+tu2tocUNrSAG0+vypw2azC5sLlyEEKUQEjdjgUyrYh6XIaS6v+TuwABgcdq2MbgiFtjoTlKEDBSeDXPBwlFbRlXJbCcJQS4v8Sjz3pIffX5Pm9lqJOFDOM0Tl6qs6lqVt5SvCUn2pvXLVkVTLriAny0IHljHOa0k9U0nSCiW0+ngWhF3kEkJU6lI/Uc1sxaln7ujBUnykqPFaa0BefN029NSQnzpxC0lIySk8VuS2XFuXEcWkJKmmtqMp9q0FAWvptvCCY92fQ9LWFJOGyNoBpSsjynpLiQcea2EqGPQdqKSW0l5xahlS1kqpXsjCGFLlqyOUhs+g5wadgjcHZKCWbY0VOvtOg4CiMD2FOa2BAanRzkB5nKRn1HtSOdkK5vNrSQFugI59CKU4xQiclIyGiopIJ5xVl6ZQBAO6UIgSuMRk4SlJxn+oUpPqU7alFZUUx5G5WO3NJ0BOJT0Yg+W4lXlpHfIpQaO6NMiLzsdZKuByFpp3BARHdW0/YnybqrxOphWxa0pmJeROSlOQWVtEHIrphtcDGrrvDkpViyy3jlfcIBOOa52PsC4bEvxVX1txHmLRplS2jns4OAa6WeqUI6S1Dq66oQW03C3hKVDsHSv4voa2FkJkpiz/AHlcx6gndHcntby7A/RRh1ImQ7fZN2R/zmZjjjpSnlQzxj9BUxPD9rb7jfLXJU5tamJSzOQU8FJHfHpj3qKSZ0C4SVKSQVEYVhXFbG0ZPh2We0FOp2uJUGkBWMHFaWrga6mLFYSU7G0ukcqcXXa2xLzp+YygIeRIJUlY74I96qJ6w6bd6TN6U6yWlnZEtdxEfWbBztchOqCc/LacHNTy071cjaxjPaSkOoTc42URnHDwpIyB+fFNvqloOFr7oxrzRMlpuQ7crBOjMoJ5S8GyptQPodwGDWIqIXU0wB8rKaHw3JoPB2UTde6lten9TdOdSIfQux6ympt70kkFBeeQFMHPtninB18tSR066irS2FpnaDkq7cFacbSP2qqSf1Zucvwgs2y/OyHNY9Ktbwgh1eQ40uFK2gK9/h9+9Wx3y9QuofQO7XqI4FImdNXX1OE9klkFY/PNTZzrpSw8tyP4VzPC6lqGlw2yuJjxBvtP9ZNaLHxfd4sVKD88HNaVDLb0dZORlwJO01tjrxFW11Z1e4rcPvJa3An0wcVqnYpiKy2OFOKKjkftXPZIXlxK6XE9vphGG2EIiEbf5ZWRzya8lRtkRpkJ25WVj8x2o0AossMLJ2HCiMc5PejsiOt4KKePLZBR/vTsTWuaER3K1XqIffLhY7c6NyG47q30Y9T2NfaTeLbq46lHcy6c5Hz4o8qP9/1dcnkYUzAjtNgD+njn/Wm8txyz3txbaSGn3PiyM5/KqUtMNf6h+6lBhIW0ry8EIS40o5DW4buex5qPet5QU7PAOUYGAQO2K3RKmIlQFOpJ+FpSU+3Peo46slqPnDcPiO0jHoKY6gqA+LbunWjsmbb1qbeSEKyEng4p15W58R5JHJppwUpC0KT6n3p6tJSG08Z49RWRbnSEZ5X0121Nk/FgknduOf2ppT5kJeUNq3AqGdp47UcXC83zPNRgnJBC6akyIpD3ltA7gMn4s05kHc8olv8A0hIaZsEJpJIQ6StaQfXNbs0+4n7m75ZIUoHPrUctNKdbtkBClpSEpGEq71vPTjziGClKwQogEhNaqxzk+0pJe0FGZ6nEKTjG9R5GKXoPmfc22VDCslR4/qzkUnTWPMmtpSFHkZwPSnHDaSClRB3ZwQT7VpWMPqFFrCcNzQw6Yc0kKccjILxTx/NTwePb5ULIDbTrDm7cpSGlpUngYP4uKRVuK2Pg5ICcjPbnvRf74oxopVlS20uAg/8A61M1gbI8Ep3hxTNzbkNrShlDg255+FXvR9oKRLXvII85QbITxtNNsLceiQ5a+Uup2qCRt+JH/rS+6pTaYz25K0lCFoO39x86WN0MjKuq+wIccj+Mi6Qi6hlMi1KRhwd0n0rsS6/+HfUWr7dMXpfZLM9rLkcqG5DnfOfYnHFcXf2G7kKV454zFweeZRMshci+S+WwpY/Kv0CoIisxW8yFlIQASXck47ZNXFBUywsBZyP5XNep2tZctu+P2XPdqLpL1R6ZqUdTaanmMypQ8+Gnfkf3HHpWsblqyZEaVJS6plQKhk/CU4GMEH1rox1BFsN0S+y9DYmLdSQpTze//WoUdTvC7ovVy5LjNnjx5MlJyWk7Rn3wK2tNeGTNHqNUJl6fC7JGQqO//bONJy2L5Hn4mW2cvC1PAgJxkqX7p+dSosPjC0XH07Hvt9uLMGPenmgl4BTrZc29kbQc55rfFr+zs0vJh3CVd7YzIaXNUhQcSSlTJPKT7imjrPwqaYtP8P0AzaLfGlt35dyt7EKEGWxBaZKvgT22owAfzpiqZS182DjCRWVcNwfG5rS0/UKnfqnoN1zSfiM1NDiuRtK6x1kufpQPs7QppRSokDHYqyald0N1e/J8H2tS4vEy36UVFbUfXepKe3tg1snxFaajRuiWobehlIjx7YPKQE4xz6H1FaD6IwnI3h21DYEIddfu9wjsRo6BlTiVHkD9hVXWtbHWiJvBwr2veJrY0+CuaDxW2Vq09YtWRkqBP8lTJSc4ynt+lRskgrlxWj2S22kj8+9TD8amlbrpfxFawsd7adRNZltqcacHKUrQFJH7GojKSf4jtV/SoYGMdu1ZOuibFUOZjgrW0LxJRsI8BCuJUHggDstKWR8qGccKfOO4ILDmHyrtswSaOQGxMnNb8ARypaj2B9qaetLgLdY7jMSsNqfeKU55OTxVZMWx0xIUtI2iG1yJF3kHG27zXA2tXyOB/vRHVsRiNuQ8QH47mCd39VK2lv8AKWeE8n4UpSlRz33DmtSdVr++q8tlpagy+0C6kd92Oao66RkFt1O5KebwE6ItyQILqHn0BASdoSADWgtTzUrkFoOBW509uKJvailBstId2tkcjuabLqzJcClqUrBznd61kaipM7AEpOG3AFxCQTt3GnohwJSAcdvemLBUpC0bTjCsDineglSEqJyTn6VGRJguzZKd4D7m7Ax8VK1uYStKX3Cpbis53HIxTfSgOSSFE4OBgGncykNtoCc8J9aCCdtuUooSMkBBwnFb60w2Ewwsk45PJ9c1Hq1OrJ2k5BUPSpEafWpEBpsH4VoJVkVprHgPBTDuU6G1AvKfHKwkevGaXoraEx3XVAhaiktYPG496bEdZ8ndxlSsGnC24r7tFTnILiiRj27Vr2OGdSJKJ2stoDoBTJJSjPfNJjSN5faX2bORijN1cUUwBwPLSHEkDncrk1iobVlaSQXUDePSjcQTlPjhLMdGbW4lHPkKBSD6bu9GfNQq1IQCrzWHCkZ/tV3onanFLauLSjlAjbgMeo7UNEQlUCQ8rJUEIUATxkn2p1kg0hAt7qyz7I7U50740um0hKiHHvMZ+H+0+/71+iTp+BKukCPIWpQbeaBSnHOK/OS+yfbRJ8c3TOK6AplU5fwY9iK/Se0/hiBCabSlKEREBIx8qsaSdwhwPJXOuqWN/uQP0CLxNLNtjcpGSSckmjUmzW+E0p95ptWzkbu/bmlOVKdQCUlOQD/T8qY16uEp8hlax5amCSEjHPan4jO5xJdsFnD6bRwl+32xmZY0uoaCWnn3DtA9M1A7xhw5Gn5nT2/2uQ5aJVjZvMifeIsRLy0w/upCmCg8KC1bf2qwF2Su0aTtqoiGySxnDwJGc/IioCePW7yrPoDUuo4yWXplg6LX6fAjSUlTH3kAJClJBBOB6Zordcni5ae2T/CnikOkfXH7KrzxJwlSukqyCM31mLlCARhDjYUU4/pPPb0pA8G+kAqdYLfItyboqO6++zAc/DvQkeWpXyyay1ZqG4au8O/S7UN28k3C82WE/NMdspQXNgHAJPGPnUy/A/p21C3Xm9fdgbiy+hlp84+FtSQSB+taOt2rA4+Mfspdx1wW4NJ7rj5+1vt863+O/rK3c47USYiVFdUwygJShJYTgAVVy0kOyX5DmcpirWCOPiz8NXFfbZMtp+0C65uAYUIdq5B75jjNVAR2kfcpznO5CAlJz6Csxd2ltxI8rY2WTXbIh30hZ2oLUJSlbsIY5IGOSOa071Vlrk/wWzMKx58pLj6B3KQeK3NblEQJzowFqeSCcehqPGqZLr3UZEdZBaiRW/JSB/cDnNZW+yujiDW91bJ7Myw1DSylWxtCBgCtSa7h/wARSh9vHmsgjd34p/BaiH0Z+FslKR8qYt5US2Qf7z/vWYuMjzTBpT7eFHWU+4XnEY2BtWMba8YeKcAnsaVNWMojTHFs5QVJSSB2ye9IDRJ2E9ynn9qzxkaErG6dUSSEhKiQpQV2p2w5a3WEq8sgBRAyKYcVISQsZKvnTmanvoQEp2AA8AIxR6wj0HK//9kA)
Prof. Mervat Fashal
Professor in Department of Phonetics and Linguistics, Faculty of Arts, Alexandria University, Alexandria, Egypt
Speech title: Human perception vs machine interface: an overview
A skill which a healthy human being acquires with no effort has proved remarkably difficult to achieve by machine. Digit recognition still couldn't solve some problems of speech recognition, that is due to various types of variabilities ( e.g., speaker, gender, emotional states, social background, different tempos, noise background, ….) Since statistical models try to simulate what happens in the auditory neural processing of speech perception, this paper may shed some light on the basic dimensions of human perception. In addition, it is an attempt to follow the progress of an incoming sound up to the neural pathways which link the cochlea to the brain from the Neurophysiological and Psycho-acoustic viewpoints. This may participate in solving the problems faced by machine recognition or could attract attention to the reasons of the low score as a result of a statistical model application.