Logically, we started to think about what could be done in order to prevent children from watching inappropriate video content. Maxdome already has a security pin code system in place which needs to be entered manually – not a pleasant user experience. What if Alexa could become the gatekeeper?
Obviously, saying out loud your pin code in front of your family is not an option, so I needed to find something cleverer: some kind of voice based captcha system.
The making of the team
On a flipchart I started to brainstorm with my team. I wanted us to focus on how a machine could differentiate an adult from a child.
- Voice Signal Frequency recognition?
The average man’s speaking voice typically has a fundamental frequency between 85 Hz and 155 Hz. A woman’s speech range is about 165 Hz to 255 Hz, and a child’s voice typically ranges from 250 Hz to 300 Hz and higher. This was not an option because too complex for a hackathon.
- Voice recognition?
Available in the US since a couple of days but not in Europe yet, requires training and is not practical when the authorized person is not at home.
- Security question?
Children learn counting only when they go to school, so asking a child how much makes 4 + 5 for example, could only get answered by people from age 6 and above: perfect for FSK 6 – content that is recommended for 6 years old at least. So I suggested we ask questions that only a person of a certain age can answer. We then defined further questions for FSK 12, FSK 16 and FSK 18.
We picked up some movies for each category from the Maxdome catalog and got started.
Setting the objectives and start coding
Our objective was to present a working prototype and we only had a few hours left. Therefore, I defined everyone’s to do list. Two of us would work on the back-end, dealing with the query and the API, while the interaction designer and I would define the interaction flow, the actions that fulfill a user’s spoken request (called “intents”) and perform some research on how people, and especially children, consume video content.
A few Red Bulls and pizza slices later, I organized a status update to see what would be achievable and what we would need to leave apart. Setting up the environment already had cost us a lot of time and nobody in the team had experience with “AWS Lambda”, the back-end language used to code “skills” for Alexa. I sat with the developers to see where they were stuck and helped them find workaround solutions, strong enough for a demo.
Since the presentation format was a four minutes pitch on stage, I also put the presentation together. A quick internet research revealed that not only 62% of parents say age-inappropriate content is their top concern but also that 63% of teens believe that accessing inappropriate content online accidentally is an issue.