Table 1.

F1-scores using majority voting (MV) and DeID, for speaker disentanglement through ADV and USSD using the DAIC-WOZ dataset. Recall that, unlike ADV, USSD does not use speaker labels for disentanglement. The parameter count for USSD does not include speaker ID models, as they are neither retrained nor fine-tuned. The best results are bold-faced.

Feature	Model	Disentanglement	Number of Parameters ↓	F1-AVG (MV) ↑	F1-ND ↑	F1-D ↑	DelD ↑
Mel-Spectrogram		No	280 k	0.658	0.756	0.560	NA
	CNN-LSTM	ADV	293k	0.694	0.773	0.615	14.01%
		USSD	280 k	0.683	0.783	0.583	10.29%
		No	515k	0.709	0.809	0.609	NA
	ECAPA-TDNN	ADV	529k	0.746	0.826	0.667	3.69%
		USSD	515k	0.746	0.826	0.667	5.97%
Raw-Audio		No	445 k	0.669	0.792	0.546	NA
	CNN-LSTM	ADV	459 k	0.709	0.809	0.609	55.83%
		USSD	445 k	0.746+	0.826	0.667	45.35%
		No	595k	0.694	0.773	0.615	NA
	ECAPA-TDNN	ADV	609k	0.790	0.880	0.700	22.32%
		USSD	595k	0.773+	0.851	0.696	19.90%
		No	1.15M	0.694	0.773	0.615	NA
ComparE16	LSTM-only	ADV	1.18M	0.762+	0.857	0.667	68.37%
		USSD	1.15M	0.776	0.885	0.667	92.87%
		No	3.6M	0.683	0.783	0.583	NA
Wav2vec2	LSTM-only	ADV	3.7M	0.747	0.863	0.632	52.43%
		USSD	3.6M	0.720	0.840	0.600	58.65%

The symbols

^{‘↑’ and ‘↓’}indicate a higher or lower value is better, respectively.

⁺indicates improvements are not statistically significant.