Robust speech recognition using consensus function based on multi-layer networks

TL;DR

This paper introduces a new consensus function for clustering ensembles using multi-layer networks and a maintenance database to improve robustness and accuracy in noisy speech recognition tasks.

Contribution

It proposes a novel consensus function based on multi-layer networks combined with a maintenance database to handle noisy speech data effectively.

Findings

Effective in handling noisy speech data

Improves clustering stability and accuracy

Validated with empirical tests on Aurora speech databases

Abstract

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.