Unsupervised neural and Bayesian models for zero-resource speech processing
Table 3.4: ABX error rates (lower is better) of models by others (Renshaw et al., 2015; Thiollière et al., 2015) on English and Xitsonga data. Both within-speaker and across-speaker discriminability are shown.
['Model', 'English ABX (%) Within', 'English ABX (%) Across', 'Xitsonga ABX (%) Within', 'Xitsonga ABX (%) Across']
More importantly, Renshaw et al. compared the cAE to more models using ABX evaluation on data from two languages: English and Xitsonga. found that, as in this chapter, cAE representations are more discriminative than MFCCs as well as representations from stacked AEs on both languages. Importantly, they also showed that the cAE outperforms a denoising AE (dAE). the cAE uses ‘realistic’ noise obtained from data, instead of using artificial noise as in the standard dAE. Instead of using a reconstruction criterion as in the cAE, they used Siamese networks: tied networks that take pairs of speech frames and minimize or maximize a distance depending on whether they come from same or different word classes (as predicted by UTD).