{"id":229,"date":"2023-04-22T13:46:34","date_gmt":"2023-04-22T17:46:34","guid":{"rendered":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/?p=229"},"modified":"2023-04-22T13:47:23","modified_gmt":"2023-04-22T17:47:23","slug":"nina-duans-status-report-for-4-22-2023","status":"publish","type":"post","link":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/2023\/04\/22\/nina-duans-status-report-for-4-22-2023\/","title":{"rendered":"Nina Duan\u2019s Status Report For 4\/22\/2023"},"content":{"rendered":"
Personal Accomplishment<\/b><\/h6>\n
    \n
  1. Order termination<\/strong><\/li>\n<\/ol>\n

    I added to our system the ability to terminate the speech recognition system. By calling this method, all background threads and the currently-running interaction will be terminated. The order information that belongs to the current interaction will also be deleted. When our new distance sensor detects that the customer has walked away, our system will use this functionality to terminate the current order interaction.<\/span><\/p>\n

      \n
    1. Testing<\/strong><\/li>\n<\/ol>\n

      The first test I conducted involved the latency between the customer-side uploading an order and the staff-side receiving the uploaded order. To calculate the difference, I printed the time when the order was sent and the time when the order was received. \u201cTime\u201d is defined as the amount of time, in seconds, since the epoch (same as how Unix defines time). I conducted two groups of ten tests (twenty in total) and received varying results.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      \n

      CMU-SECURE (4\/12\/2023)<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      Trial #<\/span><\/p>\n<\/td>\n

      		Time Sent<\/span><\/td>\nTime Arrived<\/span><\/td>\n\n

      Total Time (Time Arrived – Time Sent)<\/span><\/p>\n<\/td>\n<\/tr>\n

      1<\/span><\/td>\n1681311756.920146<\/span><\/td>\n1681311757.988204<\/span><\/td>\n1.068058<\/span><\/td>\n<\/tr>\n
      2<\/span><\/td>\n1681311787.9240708<\/span><\/td>\n1681311788.301242<\/span><\/td>\n0.377171278<\/span><\/td>\n<\/tr>\n
      3<\/span><\/td>\n1681312039.466178<\/span><\/td>\n1681312040.4595578<\/span><\/td>\n0.9933798313<\/span><\/td>\n<\/tr>\n
      4<\/span><\/td>\n1681312140.5965528<\/span><\/td>\n1681312144.366512<\/span><\/td>\n3.769959211<\/span><\/td>\n<\/tr>\n
      5<\/span><\/td>\n1681312195.167861<\/span><\/td>\n1681312197.255147<\/span><\/td>\n2.087286<\/span><\/td>\n<\/tr>\n
      6<\/span><\/td>\n1681312260.151395<\/span><\/td>\n1681312263.733936<\/span><\/td>\n3.582541<\/span><\/td>\n<\/tr>\n
      7<\/span><\/td>\n1681312359.745095<\/span><\/td>\n1681312360.1503391<\/span><\/td>\n0.405244112<\/span><\/td>\n<\/tr>\n
      8<\/span><\/td>\n1681312407.597444<\/span><\/td>\n1681312417.1346428<\/span><\/td>\n9.537198782<\/span><\/td>\n<\/tr>\n
      9<\/span><\/td>\n1681312475.726104<\/span><\/td>\n1681312478.991681<\/span><\/td>\n3.265577<\/span><\/td>\n<\/tr>\n
      10<\/span><\/td>\n1681312525.983286<\/span><\/td>\n1681312526.7069042<\/span><\/td>\n0.723618269<\/span><\/td>\n<\/tr>\n
      Avg.<\/span><\/td>\n2.581003348<\/strong><\/td>\n<\/tr>\n
      Median<\/span><\/td>\n1.577672<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

       <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      \n

      CMU-SECURE (4\/17\/2023)<\/span><\/p>\n<\/td>\n<\/tr>\n

      Trial #<\/span><\/td>\nTime Sent<\/span><\/td>\nTime Arrived<\/span><\/td>\nTotal Time (Time Arrived – Time Sent)<\/span><\/td>\n<\/tr>\n
      1<\/span><\/td>\n1681743612.060843<\/span><\/td>\n1681743612.5518022<\/span><\/td>\n0.4909591675<\/span><\/td>\n<\/tr>\n
      2<\/span><\/td>\n1681743614.6954062<\/span><\/td>\n1681743615.077455<\/span><\/td>\n0.3820488453<\/span><\/td>\n<\/tr>\n
      3<\/span><\/td>\n1681743618.92501<\/span><\/td>\n1681743619.474676<\/span><\/td>\n0.549666<\/span><\/td>\n<\/tr>\n
      4<\/span><\/td>\n1681743624.654081<\/span><\/td>\n1681743625.0546181<\/span><\/td>\n0.400537014<\/span><\/td>\n<\/tr>\n
      5<\/span><\/td>\n1681744253.4430232<\/span><\/td>\n1681744254.947194<\/span><\/td>\n1.504170895<\/span><\/td>\n<\/tr>\n
      6<\/span><\/td>\n1681744293.227913<\/span><\/td>\n1681744294.318719<\/span><\/td>\n1.090806<\/span><\/td>\n<\/tr>\n
      7<\/span><\/td>\n1681744319.980497<\/span><\/td>\n1681744320.395576<\/span><\/td>\n0.415079<\/span><\/td>\n<\/tr>\n
      8<\/span><\/td>\n1681744338.203062<\/span><\/td>\n1681744338.6219149<\/span><\/td>\n0.4188528061<\/span><\/td>\n<\/tr>\n
      9<\/span><\/td>\n1681744356.008338<\/span><\/td>\n1681744356.6730611<\/span><\/td>\n0.6647231579<\/span><\/td>\n<\/tr>\n
      10<\/span><\/td>\n1681744377.0767202<\/span><\/td>\n1681744378.1253068<\/span><\/td>\n1.048586607<\/span><\/td>\n<\/tr>\n
      Avg.<\/span><\/td>\n0.6965429493<\/strong><\/td>\n<\/tr>\n
      Median<\/span><\/td>\n0.5203125838<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      I then tested our speech recognition system for audio-to-text accuracy.<\/span><\/p>\n

      Due to the nature of our system, we mainly care that the speech recognition system recognizes the correct word, not its verb tense or singularness. Therefore, verbs of different tenses will be considered as the same word (e.g. \u201cwake,\u201d\u00a0<\/span>\u201cwoke,\u201d \u201cwaken\u201d are considered the same). Similarly, we won\u2019t distinguish between singular and plural nouns (e.g. \u201chamburger\u201d and \u201chamburgers\u201d are considered the same).<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      Sentence Spoken<\/span><\/td>\n# of Words Spoken<\/span><\/td>\nSentence Recognized<\/span><\/td>\n# of Words Correctly Recognized<\/span><\/td>\nAccuracy (Words Spoken\/Words Recognized)<\/span><\/td>\n<\/tr>\n
      \u201cI\u2019d like two hamburgers.\u201d<\/span><\/td>\n5<\/span><\/td>\n\u201cI like to hamburgers\u201d<\/span><\/td>\n3<\/span><\/td>\n60%<\/span><\/td>\n<\/tr>\n
      \u201cI want two cheeseburgers.\u201d<\/span><\/td>\n4<\/span><\/td>\n\u201cI want to cheeseburger\u201d<\/span><\/td>\n3<\/span><\/td>\n75%<\/span><\/td>\n<\/tr>\n
      \u201cOne beautifully-packaged chicken sandwich, please.\u201d<\/span><\/td>\n6<\/span><\/td>\n\u201c1 beautifully packaged chicken sandwich please\u201d<\/span><\/td>\n6<\/span><\/td>\n100%<\/span><\/td>\n<\/tr>\n
      \u201cI want to order a hundred cheesecakes.\u201d<\/span><\/td>\n7<\/span><\/td>\n\u201cI want to order 100 cheesecake\u201d<\/span><\/td>\n7<\/span><\/td>\n100%<\/span><\/td>\n<\/tr>\n
      \u201cGet me two hamburgers.\u201d<\/span><\/td>\n4<\/span><\/td>\n\u201cGet me to Hamburg\u201d<\/span><\/td>\n2<\/span><\/td>\n50%<\/span><\/td>\n<\/tr>\n
      \u201cI\u2019d like one fries and three fountain drinks.\u201d<\/span><\/td>\n9<\/span><\/td>\n\u201cI like 1 fries and 3 fountain drinks\u201d<\/span><\/td>\n8<\/span><\/td>\n88.9%<\/span><\/td>\n<\/tr>\n
      \u201cCheck out.\u201d<\/span><\/td>\n2<\/span><\/td>\n\u201cCheck out\u201d<\/span><\/td>\n2<\/span><\/td>\n100%<\/span><\/td>\n<\/tr>\n
      \u201cHello, let\u2019s go with four tacos and three ice creams.\u201d<\/span><\/td>\n11<\/span><\/td>\n\u201cHello let\u2019s go with 4 taco and 3 ice creams\u201d<\/span><\/td>\n11<\/span><\/td>\n100%<\/span><\/td>\n<\/tr>\n
      \u201cI\u2019d like one cup of coffee.\u201d<\/span><\/td>\n7<\/span><\/td>\n\u201cI like 1 cup of coffee.\u201d<\/span><\/td>\n6<\/span><\/td>\n85.7%<\/span><\/td>\n<\/tr>\n
      \u201cFifty corn dogs.\u201d<\/span><\/td>\n3<\/span><\/td>\n\u201c50 corn dog\u201d<\/span><\/td>\n3<\/span><\/td>\n100%<\/span><\/td>\n<\/tr>\n
      Avg.<\/span><\/td>\n87.9%<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      This is not a comprehensive test. We will continue to monitor the accuracy of our speech recognition system as we start integration testing.<\/span><\/p>\n

      About Schedule<\/b><\/h6>\n

      I am on track with the schedule.<\/span><\/p>\n

      Plans for Next Week<\/b><\/h6>\n

      Other than attending the final presentations, my teammates and I will start conducting integration tests with volunteers.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

      Personal Accomplishment Order termination I added to our system the ability to terminate the speech recognition system. By calling this method, all background threads and the currently-running interaction will be terminated. The order information that belongs to the current interaction will also be deleted. When our new distance sensor detects that the customer has walked … Continue reading “Nina Duan\u2019s Status Report For 4\/22\/2023”<\/span><\/a><\/p>\n","protected":false},"author":687,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[7],"tags":[],"_links":{"self":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/posts\/229"}],"collection":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/users\/687"}],"replies":[{"embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/comments?post=229"}],"version-history":[{"count":3,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/posts\/229\/revisions"}],"predecessor-version":[{"id":232,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/posts\/229\/revisions\/232"}],"wp:attachment":[{"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/media?parent=229"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/categories?post=229"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s23-teamd3\/wp-json\/wp\/v2\/tags?post=229"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}