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[hts-users:01799] Re: F0 modelling


That's a very clear, concise explanation, Heiga. I've wondered about this in the past too -- it seems amazing to me that you don't get far too many voiced/unvoiced transitions.

In effect, you're making the voiced/unvoiced decision independently for each frame. Since you're maximizing, the resulting voiced/unvoiced decision is at least constant in each state, but I'm still surprised it we don't get far too many voiced/unvoiced transitions at state boundaries. I guess there must be enough information in the full-context models to make a very reliable voiced/unvoiced decision, independent of the voiced/unvoicedness of neighbouring frames/states?

Thanks,

Matt Shannon


Heiga Zen (Byung Ha CHUN) wrote:
Hi,

Javi Palenzuela wrote:

After reading the papers, the mailing list and looking at the code, I
still don't understand at 100% how the voiced/unvoiced decision is
practically trained using MSD.

I see that the decision is made using the weight of the mixture of the
first stream. In the data, LZERO is used to flag the unvoiced regions.
But how is the weight updated accordingly with this?

Frames containing LZERO as their F0 values are unvoiced. Other frames are voiced. By running the Forward-Backward algorithm, you can get the state occupancy probability for each frame. Using state occupancy probability, you can get the following information:

#UV -> total occupancy counts of unvoiced frames assigned to this state
#V  -> total occupancy counts of voiced   frames assigned to this state

Then, you can estimate weights for V/UV as follows:

weight_uv = #UV / (#UV + #V)
weight_v  = #V  / (#UV + #V)

At the synthesis stage, if weight_v>=0.5, this state is treated as voiced, otherwise unvoiced.

Regards,

Heiga ZEN (Byung Ha CHUN)


Follow-Ups
[hts-users:01800] Re: F0 modelling, Heiga Zen (Byung Ha CHUN)
References
[hts-users:01797] F0 modelling, Javi Palenzuela
[hts-users:01798] Re: F0 modelling, Heiga Zen (Byung Ha CHUN)