Personal Accomplishments
This week, I completed research for the microphone and sound shield we need to purchase. We will proceed with Neat Bumblebee II and Moukey Microphone Isolation Shield. Both received a lot of positive customer feedback on Amazon and can arrive within 5 business days once the order is placed. Neat Bumblebee II is one of the only USB-compatible desktop directional microphones that can rotate vertically, allowing us to accommodate customers of different heights and with accessibility needs. According to past customers, it is also proficient in filtering out background noises. The Moukey Microphone Isolation Shield has 5 foldable panels, which can turn it into a semi-parabolic sound shield if necessary. I also added backup options for both microphones (Blue Yeti) and sound shields (IDoon or Aokeo) in case these two cannot satisfy our requirements after testing.
To prepare for the upcoming design review, I collaborated with my teammates to design the block diagram. I was also in charge of writing user requirements and solution approaches for the speech recognition system, and modifying our Gantt Chart schedule. Moreover, I have started doing research for implementing the natural language processing system next week.
Relevant Courses
Natural Language Processing (11-411) helped me to come up with the natural language processing pipeline for our project. Web Applications Development (17-437) which I’m taking this semester allowed me to brainstorm ideas for the kitchen-side user interface.
Schedule
According to our plan, preliminary research is to be completed by 2/19/2023, and I have done the required research for hardware components and libraries. One modification we made on the plan was that we will wait for the feedback from our design presentation to purchase the parts instead of doing it this week. Overall, I am on track with our schedule.
Plans for Next Week
I will start programming the natural language processing system that converts text to menu items next week. By next Saturday, my program should be able to identify order items in text input with around 70% accuracy.