Earlier quoted context omitted.
When I went to Portugal I was struck by how much Portuguese there does sound like Spanish with a Russian accent!
Part of this is the "dark L" sound
How AI hears accents: An audible visualization of accent clusters
21–30 of 133 posts
Re: How AI hears accents: An audible visualization of accent clusters
#22Apparently Persian and Russian are close. Which is surprising to say the least. I know people keep getting confused about how Portuguese from Portugal and Russian sound close yet the Persian is new to me.
Re: How AI hears accents: An audible visualization of accent clusters
#23Re: How AI hears accents: An audible visualization of accent clusters
#24Irish accent appears to break it.
Re: How AI hears accents: An audible visualization of accent clusters
#25The source code for this is unminified and very readable if you’re one of the rare few who has interesting latent spaces to visualize. https://accent-explorer.boldvoice.com/script.js?v=5
Re: How AI hears accents: An audible visualization of accent clusters
#26Re: How AI hears accents: An audible visualization of accent clusters
#27it would've been nice to be able to visualize the differences between the different accents in the spanish language, really cool tho
Re: How AI hears accents: An audible visualization of accent clusters
#28The source code for this is unminified and very readable if you’re one of the rare few who has interesting latent spaces to visualize. https://accent-explorer.boldvoice.com/script.js?v=5
Good catch. I really hate javascript so i never got into d3js, so plptly was such a life saver.
Re: How AI hears accents: An audible visualization of accent clusters
#29Apparently Persian and Russian are close. Which is surprising to say the least. I know people keep getting confused about how Portuguese from Portugal and Russian sound close yet the Persian is new to me.
Turkish and Persian seem to be the nearest neighbors.
Re: How AI hears accents: An audible visualization of accent clusters
#30Then I was able to apply UMAP + HDBSCAN to this dataset and it produced a 2D plot of all my books. Later I put the discovered topic back in the db and used that to compute tf-idf for my clusters from which I could pick the top 5 terms to serve as a crude cluster label.
It took about 20 to 30 hours to finish all these steps and I was very impressed with the results. I could see my cookbooks clearly separated from my programming and math books. I could drill in and see subclusters for baking, bbq, salads etc.
Currently I'm putting it into a 2 container docker compose file, base postgresql + a python container I'm working on.