Speaker-independent vowel classification
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 Washington
- Organisation type
- Academia
- Country
- United States of America
- Published
- 27 November 1989
- Authors
- L. Atlas, R. Cole, J. Connor, M. El-Sharkawi, R. Marks, Y. Muthusamy, E. Barnard
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech recognition (ASR)
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.
- Parameters
- 3K
- Training data
- 4,104 tokens
- Epochs
- 100
“The MLP consisted of 64 inputs (the DFf coefficients. each nonnalized between zero and one), a single hidden layer of 40 units, and 12 output units;”
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 7.5 × 10⁹ FLOP
- How it was established
- Operation counting
2*3040*3*410400=7485696000=7.49e9 “The network was trained on 100 iterations through the 4104 training vectors.”
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
- Historical significance
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Speaker-independent vowel classification was published by University of Washington, in the country recorded as United States of America, during November 1989. The category the publisher falls under is 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
Producing it required arithmetic totalling around 7.5 × 10⁹ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 4,104 tokens of text.
Its inclusion criterion: historical significance.
Answers
Speaker-independent vowel classification — common questions
Speaker-independent vowel classification— who created it?
It was published by University of Washington, based in United States of America, an organisation categorised as academia.
Speaker-independent vowel classification— when was it released?
It was published in November 1989. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Speaker-independent vowel classification— 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.
Speaker-independent vowel classification— how much compute was used to train it?
Training consumed around 7.5 × 10⁹ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Speaker-independent vowel classification— 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.
Speaker-independent vowel classification— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Speaker-independent vowel classification— how many parameters does it have?
It has a parameter count of 3K. “The MLP consisted of 64 inputs (the DFf coefficients. each nonnalized between zero and one), a single hidden layer of 40 units, and 12 output units;”. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
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.