Yep feel free

If you’re only detecting speech, that is doable with heuristics and some napkin math, or an MLP, for simple cases. However, “detect speech in this audio” is rarely the end of the story in the real world. Next up comes transcription, sentiment analysis, tonal feature flagging, etc. all of which are currently dominated by Transformers. You’ll also see some great work in the RNN space, but Transformer-based architectures are king right now.

Some models for inspiration, https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads

MLP for speech recognition probably isn't a great solution, but if it's for a class and you can only use numpy start here: https://towardsdatascience.com/coding-a-neural-network-from-scratch-in-numpy-31f04e4d605

Yep I agree. If you learn a transferable skill that should be taken into account.

I was framing the problem in the same terms OP did.

1. What is your best guess at how much money you'll make?
2. Divide that by your best guess at the amount of time, money, and effort it will take you to compile the dataset.
3. Do the division and ask yourself if it's worth it.

The hard math is going to get you your answer. You may be able to do some fancy correlation mapping depending on the models you think will solve the problem and what data you will need. The trouble with the "shortcut" route is two-fold:

1. It may take you longer that to do the three steps above.
2. You might not get an accurate answer.

What about using a vector search engine like Weaviate? https://weaviate.io/

Grab a pre-trained autoencoder if you don't already have one and batch your images through it and into weaviate, then use it's search functionality to compute image similarity.