I'm abandoning the whole vowel recognition unless I can find something
someone else has done to base my implementation on. I've burnt too much time
on it for something that won't give a whole lot of bang for the buck. It's a
very complex problem (for me anyway).

Eric

On Thu, Jun 3, 2010 at 2:37 PM, Juan Pablo Califano <
[email protected]> wrote:

> Wow. That was really uncalled for.
>
> Anyway, if you can pre-generate samples for all vowels for all samples, I
> can't see why comparing them to the speech generated by the same system
> would  be any harder than comparing it to a number of collected profiles.
>
> >>>
> You really just need to collect profiles to match against. Record people
> saying stuff and match the recordings with the live data. When they match,
> you know what the vocal is saying.
> >>>
>
> For me, the hard part, which you seem to imply is rather simple here, is
> *matching+ the input audio against said profiles. Admitedly, I don't know
> anything about digital signal processing and audio programming in general,
> but "matching" sounds a bit vague. Perhaps you could enlighten us, I you
> feel like.
>
> Cheers
> Juan Pablo Califano
>
> 2010/6/3 Henrik Andersson <[email protected]>
>
> > Eric E. Dolecki wrote:
> >
> >> It's using dynamic text to speech, so I wouldn't be able to use cue
> points
> >> reliably.
> >>
> >>
> > Use dynamic cuepoints and stop complaining. If it can generate voice, it
> > can tell you what kinds of voice it put where. It is far more exact than
> > trying to reverse the incredibly lossy transformation that the synthesis
> is.
> >
> >
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Interactive design and development
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