An embedded segmental K-means model for unsupervised segmentation and clustering of speech
Table 1: (a) Performance of models on the two test corpora. Lower NED is better. Runtimes for SylSeg∗ are rough estimates, obtained from personal communication with the authors [24]. (b) English development set performance (%) of BES-GMM as the variance is varied.
['[ITALIC] σ2', 'Cluster purity', 'WER']
Compared to BES-GMM, ES-KMeans achieves worse purity, WER and NED, but similar boundary, token and type F-scores. This comes with a 5-time improvement in runtime. ES-KMeans achieves worse NED than SylSeg, but much better word boundary, token and type F-scores. SylSeg, however, is twice as fast. There is a sweet spot: when σ2 is too big, most tokens are sucked up by a number of large garbage clusters; when σ2 is smaller, more tokens are assigned to separate clusters. In contrast, ES-KMeans has no σ2 parameter and considers only the single closest cluster. Under these conditions, the asymptotic equivalence of BES-GMM and ES-KMeans no longer holds. When setting σ20=1 (rather than tying it), the results of BES-GMM match ES-KMeans when σ2=0.00001.