RNN for speech
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
- National Chiao Tung University
- Organisation type
- Academia
- Country
- Taiwan
- Published
- 15 May 1998
- Authors
- SH Chen, SH Hwang, YR Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Speech
- Task
- Speech synthesis
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
- 7.5K
- Training data
- tokens
"The RNN generated a total of eigt output prosodic parameters. [...] The numbers of nodes in the first and second hidden layers were determined empirically and set to be 35 and 30, respectively" Figure 1 contains an overview of the architecture. Layer 1: (102 + 35 + 1)*35 parameters Layer 2: (43 + 35 + 1)*30 parameters Output layer: (30+8+1)*8 parameters
The data base was divided into two parts: a training set and an open test set. These two sets consisted of 28 191 and 7051 syllables, respectively. Of the top 10,000 Chinese words, 15% have 1 syllable, 78% have 2 syllables, and 7% have more than two syllables. Assuming 2 syllables per word, the training set is around 14100 words.
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
- 2.3 × 10¹¹ FLOP
- How it was established
- Third-party estimation
Extracted from AI and Compute (https://openai.com/blog/ai-and-compute/) charts by using https://automeris.io/WebPlotDigitizer/.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 231
Sources
Where this record came from and when it was last checked.
- Reference
- An RNN-based prosodic information synthesizer for Mandarin text-to-speech
- Last updated
- 28 November 2025
What the numbers mean
Background
RNN for speech was published by National Chiao Tung University, in Taiwan, in May 1998. academia is the category the publisher falls under.
It works in Speech, and is recorded as doing speech synthesis.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training it took roughly 2.3 × 10¹¹ FLOP of computation — a measure of what producing the model cost, not of how fast it answers.
Answers
RNN for speech — common questions
How much compute was used to train RNN for speech?
Around 2.3 × 10¹¹ 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 RNN for speech?
None. RNN for speech 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 RNN for speech open source?
The licensing for RNN for speech 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 RNN for speech have?
RNN for speech has 7.5K parameters. "The RNN generated a total of eigt output prosodic parameters. [...] The numbers of nodes in the first and second hidden layers were determined empirically and set to be 35 and 30, respectively" Figure 1 contains an overview of the architecture. Layer 1: (102 + 35 + 1)*35 parameters Layer 2: (43 + 35 + 1)*30 parameters Output layer: (30+8+1)*8 parameters. 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.
Who created RNN for speech?
RNN for speech was published by National Chiao Tung University, based in Taiwan, categorised as academia.
When was RNN for speech released?
RNN for speech was published in May 1998. 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 RNN for speech used for?
RNN for speech works in Speech, and is recorded as handling speech synthesis. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.