fp.

Fascinating! How did you decouple the speaker-specific vocal characteristics (timbre, pitch range) from the accent-defining phonetic and prosodic features in the latent space?

oscarfree•3h ago

We didn't explicitly. Because we finetuned this model for accent classification, the later transformer layers appear to ignore non-accent vocal characteristics. I verified this for gender for example.

JakeLester•3h ago

Thank you for sharing! the 3d visual was an interesting application of the UMAP technique.

Is there a way to subscribe to these blog posts for auto-notification?

nosrepa•5m ago

Yeah, if only there was a protocol for that.

ahstilde•3h ago

why is spanish so distributed?

ilyausorov•3h ago

Good question! It's likely because there are lots of different accents of Spanish that are distinct from each other. Our labels only capture the native language of the speaker right now, so they're all grouped together but it's definitely on our to-do list to go deeper into the sub accents of each language family!

oscarfree•3h ago

Not sure, could be the large number of Spanish dialects represented in the dataset, label noise, or something else. There may just be too much diversity in the class to fit neatly in a cluster.

Also, the training dataset is highly imbalanced and Spanish is the most common class, so the model predicts it as a sort of default when it isn't confident -- this could lead to artifacts in the reduced 3d space.

zaouiamine•23m ago

This is a fascinating look at how AI interprets accents! It reminds me of some recent advancements in speech recognition tech, like Google's Dialect Recognition feature, which also attempts to adapt to different accents. I wonder how these models could be improved further to not just recognize but also appreciate the nuances of regional