Nina Duan’s Status Report For 4/22/2023

Personal Accomplishment
  1. 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 away, our system will use this functionality to terminate the current order interaction.

  1. Testing

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. “Time” 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.

CMU-SECURE (4/12/2023)

Trial #

Time Sent Time Arrived

Total Time (Time Arrived – Time Sent)

1 1681311756.920146 1681311757.988204 1.068058
2 1681311787.9240708 1681311788.301242 0.377171278
3 1681312039.466178 1681312040.4595578 0.9933798313
4 1681312140.5965528 1681312144.366512 3.769959211
5 1681312195.167861 1681312197.255147 2.087286
6 1681312260.151395 1681312263.733936 3.582541
7 1681312359.745095 1681312360.1503391 0.405244112
8 1681312407.597444 1681312417.1346428 9.537198782
9 1681312475.726104 1681312478.991681 3.265577
10 1681312525.983286 1681312526.7069042 0.723618269
Avg. 2.581003348
Median 1.577672

 

CMU-SECURE (4/17/2023)

Trial # Time Sent Time Arrived Total Time (Time Arrived – Time Sent)
1 1681743612.060843 1681743612.5518022 0.4909591675
2 1681743614.6954062 1681743615.077455 0.3820488453
3 1681743618.92501 1681743619.474676 0.549666
4 1681743624.654081 1681743625.0546181 0.400537014
5 1681744253.4430232 1681744254.947194 1.504170895
6 1681744293.227913 1681744294.318719 1.090806
7 1681744319.980497 1681744320.395576 0.415079
8 1681744338.203062 1681744338.6219149 0.4188528061
9 1681744356.008338 1681744356.6730611 0.6647231579
10 1681744377.0767202 1681744378.1253068 1.048586607
Avg. 0.6965429493
Median 0.5203125838

I then tested our speech recognition system for audio-to-text accuracy.

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. “wake,” “woke,” “waken” are considered the same). Similarly, we won’t distinguish between singular and plural nouns (e.g. “hamburger” and “hamburgers” are considered the same).

Sentence Spoken # of Words Spoken Sentence Recognized # of Words Correctly Recognized Accuracy (Words Spoken/Words Recognized)
“I’d like two hamburgers.” 5 “I like to hamburgers” 3 60%
“I want two cheeseburgers.” 4 “I want to cheeseburger” 3 75%
“One beautifully-packaged chicken sandwich, please.” 6 “1 beautifully packaged chicken sandwich please” 6 100%
“I want to order a hundred cheesecakes.” 7 “I want to order 100 cheesecake” 7 100%
“Get me two hamburgers.” 4 “Get me to Hamburg” 2 50%
“I’d like one fries and three fountain drinks.” 9 “I like 1 fries and 3 fountain drinks” 8 88.9%
“Check out.” 2 “Check out” 2 100%
“Hello, let’s go with four tacos and three ice creams.” 11 “Hello let’s go with 4 taco and 3 ice creams” 11 100%
“I’d like one cup of coffee.” 7 “I like 1 cup of coffee.” 6 85.7%
“Fifty corn dogs.” 3 “50 corn dog” 3 100%
Avg. 87.9%

This is not a comprehensive test. We will continue to monitor the accuracy of our speech recognition system as we start integration testing.

About Schedule

I am on track with the schedule.

Plans for Next Week

Other than attending the final presentations, my teammates and I will start conducting integration tests with volunteers.

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