IMPROVED LARGE-MARGIN SOFTMAX LOSS FOR SPEAKER DIARISATION
Table 2: SERs (%) for models using all parameters in GLM-Softmax. The Overlapping Speech Model uses the approach of modifying the GLM-Softmax parameters for overlapping speech samples.
['Model', 'Combined v1', 'Combined v2', 'Overlapping Speech Model']
The combined approach shows further improvements relative to the single parameter models. Combined v1 has a further 3.0% SER relative reduction over m1=1.10 (and 24.6% relative over the baseline), with Combined v2 performing competitively. The notable property about v2 is that m1<1 makes the training criteria harder as the model becomes more accurate, and m2=0.20 ensures cos(θt)⩾ψ(θt). For the Overlapping Speech Model (using the approach of changing parameters to (m1,m2,m3)=(1,0,0) for overlapping speech samples), since the AMI dataset includes a significant amount of overlapping speech, the best parameters to use are notably different from the conventional approach (of using just one set of parameters). Overall, the reduction in SER relative to the baseline is 17.0% and 40.4% for dev and eval respectively, giving an overall reduction of 29.5%.