Robust speech recognition using consensus function based on multi-layer networks
This work addresses the sensitivity of consensus functions to data quality in clustering ensembles for speech recognition, offering an incremental improvement for handling noisy speech data.
The paper tackles the problem of finding robust consensus functions for clustering ensembles in speech recognition by proposing a novel method based on multilayer networks and a maintenance database approach to handle noisy speech data. The method was empirically evaluated on distorted speech from Aurora databases, showing it generates good results and efficient data partitions.
The clustering ensembles mingle numerous partitions of a specified data into a single clustering solution. Clustering ensemble has emerged as a potent approach for ameliorating both the forcefulness and the stability of unsupervised classification results. One of the major problems in clustering ensembles is to find the best consensus function. Finding final partition from different clustering results requires skillfulness and robustness of the classification algorithm. In addition, the major problem with the consensus function is its sensitivity to the used data sets quality. This limitation is due to the existence of noisy, silence or redundant data. This paper proposes a novel consensus function of cluster ensembles based on Multilayer networks technique and a maintenance database method. This maintenance database approach is used in order to handle any given noisy speech and, thus, to guarantee the quality of databases. This can generates good results and efficient data partitions. To show its effectiveness, we support our strategy with empirical evaluation using distorted speech from Aurora speech databases.