I am confused why the speaker embedding g is used to condition multiple model components (Posterior Encoder, Decoder, Flow) as opposed to just Flow.
From the model diagram in Fig. 1 (a) (Training procedure), the speaker embedding g is used to condition the normalising Flow. This makes sense: at inference time, this information in the reversed Flow to reverse the z' distribution into a speaker-informed z which was modelled after the real data x_lin with the Posterior Encoder.
To me this seems like enough supervision, and I am confused why g is used in other places too:
- in Posterior Encoder which uses
x_lin as input, g is also supplied - but it shouldn't be needed as x_lin already contains the speaker information! (And g is not mentioned in section 2.2.2. of the paper when this encoder is discussed)
- in Decoder, similarly,
z is already informed with the speaker embedding, so why do we need to explicitly supply it here?
I am confused why the speaker embedding
gis used to condition multiple model components (Posterior Encoder, Decoder, Flow) as opposed to just Flow.From the model diagram in Fig. 1 (a) (Training procedure), the speaker embedding
gis used to condition the normalising Flow. This makes sense: at inference time, this information in the reversed Flow to reverse thez'distribution into a speaker-informedzwhich was modelled after the real datax_linwith the Posterior Encoder.To me this seems like enough supervision, and I am confused why
gis used in other places too:x_linas input,gis also supplied - but it shouldn't be needed asx_linalready contains the speaker information! (Andgis not mentioned in section 2.2.2. of the paper when this encoder is discussed)zis already informed with the speaker embedding, so why do we need to explicitly supply it here?