A Hybrid Framework for Topic Structure using Laughter Occurrences
Table 1: The baseline and proposed approach results
['Algorithm', '[ITALIC] Pk', '[ITALIC] WD']
The first row and the fourth row present the baseline performances where we used the BayesTopic and BestCluster algorithm. The second and third rows present our proposed clustering based approaches, OptimAggloCluster refers to the approach of segmentation using agglomerative clustering, then optimized with fixed boundary clumping and OptimKMedoidCluster refers to the performances of iteratively optimized of the K-medoids clustered segmentation. We present the results with two metrics in two columns. For all the five cases the Pk measure performances marginally work better than the corresponding WD measure performances. Between the two baselines, the BayesTopic was harder to overcome. OptimKMedoidCluster performs better than the OptimAggloCluster approach. The BestCluster, which is our second baseline, shows better performance than the standalone cluster based approaches but it shows lower performance than the first baseline the BayesTopic. The best performance is performed by our proposed hybrid approach hybridFrameWrk that beats both of the baselines for both of the Pk and WD measures. It is possible to use this method without any change for a range of conversational applications like hybrid, online, multilingual and low resource language systems.