Re: [orca-list] Orca: Support for Multilingual text


 As has been discused previously I think generalized voice profiles
are better than language profiles, the only real difference being that
you could have different profiles for the same language.

As for auto detection I'd be willing to consider choosing based on
char set, but I don't much like huristics beyond that.  Its
complicated, and will add a lot of complicated code.  Why don't we
just let the user tell us what language they'd like us to change to?
Humans are good at this sort of thing lets use that.  I have some
questions about the char set identification too, supose a user is
using an english language and we encounter chars that could be in
ukrainian or russian, witch do you switch to?


On Mon, Jul 19, 2010 at 09:23:42AM -0700, Bill Cox wrote:
On Mon, Jul 19, 2010 at 6:31 AM, Joanmarie Diggs
<joanmarie diggs gmail com> wrote:
Are you planing to implement the switching of languages in the synth
only or replacing orca's locale?

Can I have a third choice? <smile>

Based on my limited understanding of the problem, I suspect the best
solution would be to have "language profiles" in Orca, like described
in bug 419742, that users can switch between quickly.  In addition, it
would be great to have speech-dispatcher/opentts on the back end run a
simple heuristic to detect what language is actually being spoken, the
way voiceman does, and to temporarily switch when another language is
detected.  We could perhaps simply copy the heuristic.

If I understand correctly, there are many Russian web sites that have
a lot of English embedded, with no simple means of determining the
language, except to look at the characters used.  Since there is
little or no overlap between Russian and English letters, it's easy to
detect when to switch.  However, I've been told that it's harder to
detect the switch between Russian and Ukraine, and that users
currently manually switch in this case.

Perhaps we could do better language detection by loading the most
common 20,000 or so words per language profile, and matching each
phrase to be spoken to them.

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