We were moved by the need to connect the sponsor with their sponsored little ones in need all over the world.
What it does
It allows the sponsor to talk, in his/her native language, to their sponsored kid also in his/her native language. It also censors unwanted words or images and reports repeated offenders.
How we built it
We built it using AWS Transcribe to read the audio and turn it into text, we then used AWS Translate to translate the text into english, if not already in english. We use comprehend to look for the sentiment and if it's negative, we replace it with a custom message. If the message is not negative, we use AWS Polly to translate the text from english to the childrens language.
Challenges we ran into
AWS Comprehend wanted us to provide it with 1000 documents of profanities. We didn't have them, so we compromised on doing something simpler.
Accomplishments that we're proud of
What we learned
AWS Transcribe AWS Translate AWS Polly AWS Comprehend
What's next for Table21
We think we can improve the usage of Amazon Comprehend to identify profanity, but we need to gather samples of profanity in the english language, and we didn't have to a document of profanities. We decided to use the sentiment instead, as a placeholder for the profanity analyzer.