Inspiration
We were looking to build out additional features for existing speech recognition and conversational agents. General-purpose chatbots are few and far between, whereas closed-domain bots lack the ability to maintain a conversation any more meaningful than an automated FAQ.
What it does
Bots know what we mean based on the text we type. However, humans get and give a lot of signals from intonation; that would essentially go by the wayside with chatbots. This would aim to improve the user experience by taking that data into account.
How we built it
We used a 1D convolutional neural network with a single dense layer to predict probabilities for a given set of emotions (8 per sex). We used Flask for the Model-as-a-Service REST API. Additionally, electron was used for the frontend.
Challenges we ran into
- Training resources were not available, leading us to lose a ton of time. GPU quotas were not able to be used from the GCP credits, rendering them useless. To combat that, we had to use a manual solution on colab, and port it by hand to a compute engine.
- Issues gathering data.
- Troubles generalizing within the time available.
Accomplishments that we're proud of
- ML in 24 hours.
- A nice frontend in a library we didn't have too much experience with.
- A working demo.
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