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This module provides an overview of Team SSB's final ELEC 301 project -- accent recognition and classification with a neural network


Although seemingly subtle, accents have important influences in many areas – from business, to sociology,technology, security, and intelligence. While much linguistic analysis has been done on the subject matter, very little work hasdone with regards to potential applications.


The goal of this project is to generate a process for accurate accent detection. The algorithm developed should have the flexibility to choose how many accents to differentiatebetween. Currently, the algorithm is aimed at differentiating accents by languages, rather than regions, but should be able toconform to the latter as well. Finally, the application should produce an output showing the relative strength of a speaker'sprimary accent compared to the rest in the system.

Design choices

The agreed-upon option for achieving the desired flexibility in the project's algorithm is to use a neuralnetwork. A neural network is a matrix containing weights that correspond to how certain parameters fed to the network tie theinputs to the outputs. Parameters of known inputs with corresponding outputs are fed to the network to train it. Trainingthe network produces the weighted matrix, to which test samples can then be fed. This provides a powerful and flexible tool that can beused to generate the desired algorithm.

Utilizing this limits the project group only by the amount of overall samples collected totrain the matrix with, and how they are defined. For this project, approximately 800 samples from over 70 people have been collectedfor the purposes of training and testing. The group of language-based accents to test with consists of American NorthernEnglish, American Texan English, Farsi, Russian, Mandarin, and Korean.


Potential applications for this project are incredibly diverse. One example might be for tagging informationabout a subject for intelligence purposes. The program could also be used as a potential aid/error check for voice-recognition basedsystems such as customer service or bioinformatics in security systems. The project can even aid in showing a student's progressin learning a foreign language.

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In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
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Source:  OpenStax, Accent classification using neural networks. OpenStax CNX. Dec 15, 2005 Download for free at http://cnx.org/content/col10320/1.1
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