Back in the old days (well, when I was in university in the late 90s :)
I was working in an audio research project, and was thinking about a lot
of audio processing ideas. Obviously one of the ones that came to mind
was that of speech recognition, and I came up with an interesting idea,
albeit maybe not an original one.

After having played with (some years before that) an audio spectrum
analyser, I started to think about reducing the amount of information
in an audio signal, which, of course, is what a spectrum analyser does.
It gives you, lets say, 10 bands of information spaced every octave.
And each of these 10 bands might be connected to a level meter with 10
steps from no signal to full signal. Obviously there is not enough
information presented by this kind of display to recreate the audio,
but it does give a kind of view of the signal that is not possible in
any other way.

And that got me thinking...

If you could so easily reduce the amount of audio information with a
bunch of analog filters, it would then be more easy to process the
result in a CPU, provided that there was enough information to do
whatever task was required. And so I envisioned a system using a bunch
of filters adapted to different parts of the speech band. Some would
detect the various sounds of vowels by particular weighting of the
signal in certain bands, and some would detect consanants, etc. The
signals would just be DC signals that are slowed down enough to be
very fast level meters, but not the original waveforms themselves.
Then a CPU would just need to compare the sequence of energies measured
in the various bands against reference sequences previously recorded.
It could even deal with words it didn't know by chopping up the result
and applying it to multiple prerecorded sequences. Perhaps the analog
circuit could be replaced by a DSP, although for a prototype, I think
that it would be easier to use analog circuits.

So, I guess the long and the short of it is that I have no idea if
this is feasible, or if this might be how some software/hardware does
the job already, but it's worth a shot. For a hobby type project it
would be pretty easy to construct, and probably fairly easy to program.
Even just with a bunch of analog filters and level meter circuits feeding
their voltages into the analog inputs to a PIC, a nice little RS-232
output could be made to plug into a PC. Then you could write simple
software on the PC to watch the voice spectrum with a minimal amount
of coding and CPU effort.


Andrew



On Thu, Apr 22, 2004 at 03:18:57AM +0300, Dimitris Kapousouz wrote:
> Hello Dear list
>
> I am working on a (hobby) project, trying to implement some kind of system
> that will be able to accept orders and act accordingly depending on the
> order. To be more specific i want a -robotic- arm to move
> up-down-left-right, when i say these (up down left right) words. I decided
> to build this system with use of neural networks (i have read that it gives
> till now the most promisng results).
>
> I thought that the network should have be feedforward network trained with
> back propagation (very common for pattern recognition), but i am stuck in
> some thing. The theory of NN says that every neuron of the network should
> have a transfer function (sigmoid, pulse, logsig, linear etc.). I don't know
> which one is the best for my application. Will i understand this by
> experimenting or is there some standard guiding rule?
>
> Another question that arises is where to train the network? I know about
> Matlab and fuzzytech, but is there any other software that handles NN that i
> should for sure check out? Is there any that can also generate code for PICs
> or AVRs ? (My final wish is to get all this system in some MCU but i am not
> sure yet if there is any PIC with enough RAM and speed for such an
> application maybe dsPIC?)
>
> And a last question, which sapmling rate is reccomended for speech
> applications?
>
>
> Thank you in advance, i will appreciate any comments
>
> Dimitris Kapousouz
>
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