MLN-ASR
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
- McGill University
- Organisation type
- Academia
- Country
- Canada
- Published
- 1 August 1988
- Authors
- Renato De Mori, Yoshua Bengio, Régis Cardin
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
- 10K
- Training data
- 12,600 tokens
“For an MLN of about 10,000 links, the time was 115 CPU msecs for the recognition of a spoken letter and 317 msecs for the learning of a spoken letter on the SUN 4/280. A 20% reduction was obtained on the VAX 8650”
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
- 3 × 10⁸ FLOP
- How it was established
- Hardware
“For an MLN of about 10,000 links, the time was 115 CPU msecs for the recognition of a spoken letter and 317 msecs for the learning of a spoken letter on the SUN 4/280. A 20% reduction was obtained on the VAX 8650”, “Learning and recognition were performed on a VAX 8650.” Dataset: 70*10*2=1400 (Train) 10*10*2=200 (Test) “The ten words of the El set were pronounced twice by 80 speakers (40 males and 40 females)” “The data from 70 speakers were used as a training set while the data from the remain…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 0 hours
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
- Data-Driven Execution of Multi-Layered Networks for Automatic Speech Recognition
- Last updated
- 28 November 2025
What the numbers mean
About this model
MLN-ASR was published by McGill University, in Canada, in August 1988. The organisation is categorised as academia.
It works in Speech, and is recorded as doing speech recognition (ASR).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took roughly 3 × 10⁸ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 12,600 tokens.
The reason it appears in this catalogue at all is historical significance.
Answers
MLN-ASR — common questions
Who created MLN-ASR?
MLN-ASR was published by McGill University, based in Canada, categorised as academia.
When was MLN-ASR released?
MLN-ASR was published in August 1988. 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 MLN-ASR used for?
MLN-ASR 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 MLN-ASR?
Around 3 × 10⁸ 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 MLN-ASR?
None. MLN-ASR 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 MLN-ASR open source?
The licensing for MLN-ASR 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 MLN-ASR have?
MLN-ASR has 10K parameters. “For an MLN of about 10,000 links, the time was 115 CPU msecs for the recognition of a spoken letter and 317 msecs for the learning of a spoken letter on the SUN 4/280. A 20% reduction was obtained on the VAX 8650”. 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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