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 United States of America, in November 1989. academia is the category the publisher falls under.
It works in Speech, and is recorded as doing 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 around 7.5 × 10⁹ FLOP of arithmetic, which is a statement about the training budget rather than about inference.
Around 4,104 tokens went into training it.
Its inclusion criterion is historical significance.
Answers
Speaker-independent vowel classification — common questions
Who created Speaker-independent vowel classification?
Speaker-independent vowel classification was published by University of Washington, based in United States of America, categorised as academia.
When was Speaker-independent vowel classification released?
Speaker-independent vowel classification 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.
What is Speaker-independent vowel classification used for?
Speaker-independent vowel classification works in Speech, and is recorded as handling 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.
How much compute was used to train Speaker-independent vowel classification?
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.
What GPU do I need to run Speaker-independent vowel classification?
None. Speaker-independent vowel classification 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.
Is Speaker-independent vowel classification open source?
The licensing for Speaker-independent vowel classification was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Speaker-independent vowel classification have?
Speaker-independent vowel classification has 3K parameters. “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?
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