Greta Tuckute
@gretatuckute.bsky.social
Studying language in biological brains and artificial ones at the Kempner Institute at Harvard University.
www.tuckute.com
www.tuckute.com
Examples: audio before red line = ground-truth prompt; after = AuriStream’s prediction, visualized in the time-frequency cochleagram space.
AuriStream shows that causal prediction over short audio chunks (cochlear tokens) is enough to generate meaningful sentence continuations!
AuriStream shows that causal prediction over short audio chunks (cochlear tokens) is enough to generate meaningful sentence continuations!
August 19, 2025 at 1:12 AM
Examples: audio before red line = ground-truth prompt; after = AuriStream’s prediction, visualized in the time-frequency cochleagram space.
AuriStream shows that causal prediction over short audio chunks (cochlear tokens) is enough to generate meaningful sentence continuations!
AuriStream shows that causal prediction over short audio chunks (cochlear tokens) is enough to generate meaningful sentence continuations!
Humans largely learn language through speech. In contrast, most LLMs learn from pre-tokenized text.
In our #Interspeech2025 paper, we introduce AuriStream: a simple, causal model that learns phoneme, word & semantic information from speech.
Poster P6, tomorrow (Aug 19) at 1:30 pm, Foyer 2.2!
In our #Interspeech2025 paper, we introduce AuriStream: a simple, causal model that learns phoneme, word & semantic information from speech.
Poster P6, tomorrow (Aug 19) at 1:30 pm, Foyer 2.2!
August 19, 2025 at 1:12 AM
Humans largely learn language through speech. In contrast, most LLMs learn from pre-tokenized text.
In our #Interspeech2025 paper, we introduce AuriStream: a simple, causal model that learns phoneme, word & semantic information from speech.
Poster P6, tomorrow (Aug 19) at 1:30 pm, Foyer 2.2!
In our #Interspeech2025 paper, we introduce AuriStream: a simple, causal model that learns phoneme, word & semantic information from speech.
Poster P6, tomorrow (Aug 19) at 1:30 pm, Foyer 2.2!
What are the organizing dimensions of language processing?
We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals
We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals
May 23, 2025 at 5:00 PM
What are the organizing dimensions of language processing?
We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals
We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals