Unsupervised neural and Bayesian models for zero-resource speech processing
Table 5.2: Same-different performance of different representations. In the first two rows, DTW alignment over all frames is used for segment comparison; dimensionality (dim.) is at the frame level. In the last two rows, cosine distance between fixed-dimensional acoustic word embeddings (obtained by downsampling to ten frames) is used and the size of the embedding vectors is given. Higher AP is better.
['Features', 'Dim.', 'Metric', 'Average precision (AP) English1', 'Average precision (AP) English2', 'Average precision (AP) Xitsonga']
Here each test set contains all words of at least 5 characters and 0.5 seconds in duration from all speakers in the corresponding dataset (i.e. speaker-independent evaluation). For the cAE, we use activations from the 13-unit bottleneck layer as features, as described in the previous section. This is especially pronounced on Xitsonga, which has the lowest AP (0.281) for the MFCC features, but the highest AP (0.552) for the cAE features. The performance when downsampling each test segment to ten frames (the output of fe) is given in the third and fourth rows using the MFCC and cAE features, respectively. Again, cAE features perform best. The improvements on English2, however, are not as pronounced as they are on the other two datasets. The biggest improvement is again on Xitsonga.