Toward domain-invariant speech recognition via large scale training
Table 2: Pairwise clustering metrics between each application domain and the Telephony domain. Lower silhouette scores and higher cluster similarity indicate more overlap.
['[BOLD] Domain', '[BOLD] Average silhouette', '[BOLD] Cluster similarity']
Xi, a(i) is the mean Euclidean distance of Xi to other points in the same cluster, and b(i) is the mean Euclidean distance of Xi to the nearest neighboring cluster. The higher the silhouette score , the better the point sits within its designated cluster. The silhouette averaged over a cluster thus suggests how well-defined the cluster is; over the entire data set, it quantifies how distinct the clusters are on average. To understand how similar each application domain is to the held-out Telephony domain, we perform pairwise silhouette analysis. Rij=Si+SjMij, (2) where Si is a dispersion measure of cluster i and Mij is a distance measure between clusters i and j. Here we define Si as the average Euclidean distance of points in cluster i to the cluster centroid, and Mij as the Euclidean distance between the centroids of clusters i and j. Results from 50 examples sampled from each application domain (Tab.