Unspeech: Unsupervised Speech Context Embeddings
Table 3: Comparing clustered utterances from TED-LIUM using i-vectors and (normalized) Unspeech embeddings with speaker labels from the corpus. ”-sp” denotes embeddings trained with speed-perturbed training data.
['[BOLD] Embedding', '[BOLD] Num. clusters  [BOLD] train', '[BOLD] Num. clusters  [BOLD] dev', '[BOLD] Num. clusters  [BOLD] test', '[BOLD] Outliers  [BOLD] train', '[BOLD] Outliers  [BOLD] dev', '[BOLD] Outliers  [BOLD] test', '[BOLD] ARI  [BOLD] train', '[BOLD] ARI  [BOLD] dev', '[BOLD] ARI  [BOLD] test', '[BOLD] NMI  [BOLD] train', '[BOLD] NMI  [BOLD] dev', '[BOLD] NMI  [BOLD] test']
We can also use the generated vectors to cluster a corpus of utterances to gain insight into what kind of utterances get clustered together. We use Adjusted Rand Index (ARI) We found that Unspeech embeddings and i-vectors give a sensible number of clusters, without much tweaking of HDBSCAN’s two parameters (min. cluster size, min. samples) On the train set, Unspeech embeddings will provide slightly higher cluster scores, while i-vectors provide better scores for dev and test (that have a significantly smaller number of speakers). Unspeech-64 is slightly better than Unspeech-32 on the dev and test set. ARI is sensitive to the absolute number of outliers – we found NMI to be a better metric to compare the results on the train set.