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These things are slow (by modern standards), yeah, but it was possible to get the model this far down ... how far can we go?

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Must be a way to prune the nn with some reduction of quality. Probably a means to reduce the quality until it is on an Arduino and some like an anonymous video.

There was speech synethsis on the apple iie in like 64k of RAM and a 1mhz CPU.

> There was speech synethsis on the apple iie in like 64k of RAM and a 1mhz CPU.

Speech synthesis is far far far easier than parsing a human voice, especially when it doesn’t need to sound realistic (as was the case back then).

Current approach of speech recognition is about as old (maximum likelihood using WFST).

In the old papers about problem, there was vocabulary size of 64K words, because nothing was working for bigger vocabularies.

Pruning is a great idea to reduce memory usage. One thing to be careful with is that pruned matrices use irregular memory access and they might be slower as we don't have SIMD support for sparse matrix multiplication on generic CPU's (e.g. ARM) yet.

You may decompose matrix for each layer into two matrices with a bottleneck layer. E.g., A=BC (approximately) where A is MxN matrix and B and C are MxK and KxN matrices, where K is small enough.

It is done using SVD and improves speed without sacrificing memory access patterns.

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