Nowadays, natural language interaction comes built into a wide range of computing devices such as mobile phones, tablets and laptops. Typically usage is limited to searching for information on the internet or asking a virtual digital assistant to speed dial a specified contact. What might not be obvious is that this capability is rapidly evolving, not because of the cleverness or productivity of human programmers, but through machine learning. The introduction of natural language interaction to consumer devices that are connected to the internet means that potentially hundreds of millions of people interact with them. With artificial intelligence in the loop, the ability of software to interpret natural language is continually improving. In the future, we should expect this capability to be provided in engineering software packages such as Autocad, Abaqus or Ansys. This project involves investigating the capabilities of a number of open source toolkits for voice recognition and selecting one of them to use with ParaFEM, an open source package for finite element analysis developed at Manchester. The work will involve creating a prototype workflow on a Linux computer that takes very simple voice commands and converts them into computer commands to drive the engineering software. A good outcome from the project would be to allow a user to say “Run ParaFEM” to the computer and for the user to see the software running. Depending on the progress made, the implementation and level of user interaction could be more advanced such as using voice commands to change the value of simulation parameters.
Use a ParaFEM binary executable as a black box in windows. So you do not need to understand the finite element method in detail. Focus on the libraries for voice recognition and how to turn voice commands into Python commands. Parafem can be downloaded freely. I have also attached a book, it may be help but tutor said its not concerned too much. Also, very Important, please note that you do not need to understand the finite element method in detail. Focus on the libraries for voice recognition and how to turn voice commands into Python commands.
Remember, 25% marks for background research (why choose this project, background summary, method and why … writing like this way).
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