MS-ensemble-speech-recognition
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
- Microsoft
- Organisation type
- Industry
- Country
- United States of America
- Published
- 12 September 2016
- Authors
- Wayne Xiong, J. Droppo, Xuedong Huang, F. Seide, M. Seltzer, A. Stolcke, Dong Yu, G. Zweig
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.
- Parameters
- 3.2B
- Training data
- 11,140,000,000 tokens
Large ensemble CNNs: 85000000 + 38000000 + 65000000 = 188000000 = 188M LSTMs: 40 / 140 input - 6*512 layers - 9000 / 27000 output 4*(140+512)*512 + 5*4*(512+512)*512 + 4*(512+27000)*27000=2983117056 LM: 1000*1000 = 1000000 + Embedding Total: 1000000+2983117056+188000000=3172117056 (underestimate because it doesn't account for embeddings)
2000h of audio data + 85M words of text data
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
- Likely
https://paperswithcode.com/sota/speech-recognition-on-switchboard-hub500
Sources
Where this record came from and when it was last checked.
- Reference
- The microsoft 2016 conversational speech recognition system
- Last updated
- 28 November 2025
What the numbers mean
Background
MS-ensemble-speech-recognition was published by Microsoft, in the country recorded as United States of America, during September 2016. The publishing organisation is categorised as industry.
It works in the domain of Speech, and is recorded as performing the task of speech recognition (ASR).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training consumed a corpus of around 11,140,000,000 tokens of text.
Its inclusion criterion: sOTA improvement.
Answers
MS-ensemble-speech-recognition — common questions
MS-ensemble-speech-recognition— what is it used for?
It works in the domain of Speech, and is recorded as handling the task of speech recognition (ASR). These are the areas it was designed around; they describe intent rather than a hard boundary.
MS-ensemble-speech-recognition— 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.
MS-ensemble-speech-recognition— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
MS-ensemble-speech-recognition— how many parameters does it have?
It has a parameter count of 3.2B. Large ensemble CNNs: 85000000 + 38000000 + 65000000 = 188000000 = 188M LSTMs: 40 / 140 input - 6*512 layers - 9000 / 27000 output 4*(140+512)*512 + 5*4*(512+512)*512 + 4*(512+27000)*27000=2983117056 LM: 1000*1000 = 1000000 + Embedding Total: 1000000+2983117056+188000000=3172117056 (underestimate because it doesn't account for embeddings). 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.
MS-ensemble-speech-recognition— who created it?
It was published by Microsoft, based in United States of America, an organisation categorised as industry.
MS-ensemble-speech-recognition— when was it released?
It was published in September 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.
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.