RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\begin{table}
		\centering
		\begin{adjustbox}
			{max width=\textwidth}
			\begin{tabular}{lcccccccc}
				\toprule                      & \multicolumn{2}{c}{Male}      & \multicolumn{2}{c}{Female}                 \\
				                              & Vendi score                   & Avg. Cosine                               & Vendi score                     & Avg. Cosine                              &  \\
				\midrule

SoundStream         & \ \hphantom{-}0.544 (± 0.312) & \ \hphantom{-}0.759 (± 0.175)             & \ \hphantom{-}0.234 (± 0.426)   & \ \hphantom{-}0.731 (± 0.284)             \\
				Wav2vec-BERT                  & \ -0.448 (± 0.194)            & \ -0.419 (± 0.193)                        & \ -0.118 (± 0.258)              & \ -0.094 (± 0.245)                        \\
				HuBERT                        & \ \ -0.575 (± 0.094)          & \ -0.447 (± 0.202)                        & \ -0.178 (± 0.291)              & \ \hphantom{-}0.007 (± 0.321)             \\
				SpeechSim                     & \ \hphantom{-}0.811 (± 0.074) & \ \hphantom{-}\textbf{1.000} (± 0.000)    & \ \hphantom{-}0.834 (± 0.073)   & \ \hphantom{-}\textbf{1.000} (± 0.000)    \\
				\midrule Trained from scratch & \ \hphantom{-}0.480 (± 0.200) & \ \hphantom{-}0.962 (± 0.044)             & \ \hphantom{-}0.666 (± 0.200)   & \ \hphantom{-}\underline{0.963} (± 0.053) \\
				SpeechSim/Voice               & \ \hphantom{-}0.823 (± 0.063) & \ \hphantom{-}\underline{0.993} (± 0.019) & \ \ \hphantom{-}0.881 (± 0.077) & \ \hphantom{-}\textbf{1.000} (± 0.000)    \\
				\bottomrule
			\end{tabular}
		\end{adjustbox}
		\caption{Table 1 : Voice diversity. Average Spearman correlations ($\pm$
		standard error) between number of distinct speakers and diversity scores induced
		by speech representations. Male (Female) refer to samples with male-only (female-only)
		voices. Best results are in bold, second best results are underlined.}
	\end{table}