Segmental RNN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Edinburgh,Carnegie Mellon University (CMU),University of Washington
- Organisation type
- Academia,Academia,Academia
- Country
- United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 20 June 2016
- Authors
- Liang Lu, Lingpeng Kong, Chris Dyer, Noah A. Smith, Steve Renals
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
- Approach
- Supervised
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
https://paperswithcode.com/sota/speech-recognition-on-timit
Sources
Where this record came from and when it was last checked.
- Reference
- Segmental Recurrent Neural Networks for End-to-end Speech Recognition
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Segmental RNN was published by University of Edinburgh,Carnegie Mellon University (CMU),University of Washington, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during June 2016. It comes out of an organisation categorised as academia,Academia,Academia.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Segmental RNN — common questions
Segmental RNN— who created it?
It was published by University of Edinburgh,Carnegie Mellon University (CMU),University of Washington, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia,Academia.
Segmental RNN— when was it released?
It was published in June 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Segmental RNN— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Segmental RNN— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Segmental RNN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Segmental RNN— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.