Rashmi Gangadharaiah

Spectral Clustering for Example Based Machine Translation

Prior work has shown that generalization of data in an Example Based Machine Translation (EBMT) system, reduces the amount of pre-translated text required to achieve a certain level of accuracy (Brown, 2000). Several word clustering algorithms have been suggested to perform these generalizations, such as k-Means clustering or Group Average Clustering. The hypothesis is that better contextual clustering can lead to better translation accuracy with limited training data. In this paper, we use a form of spectral clustering to cluster words, and this is shown to result in as much as 29.08% improvement over the baseline EBMT system.

Extracting additional information from Gaussian mixture model probabilities for improved text independent speaker identification

This paper addresses the problem of robust text-independent speaker identification. A voting mechanism is proposed to combine probabilities generated using Gaussian mixture models (GMMs). This algorithm is evaluated on standard data sets and shown to improve performance. This method is found to decrease error rate by up to 68.6% relative on KING database and 34.9% relative on SPIDRE. An analysis is performed and a hypothesis is proposed as to why this algorithm does not give as good an identification rate in certain cases. A method of using voting along with the standard GMM method is described which overcomes this limitation. This second method is evaluated and found to decrease error rate by as much as 45.67% relative on the SPIDRE databases. It is found to give a substantial improvement over conventional GMMs in all the experiments performed. Both the proposed algorithms achieve increased accuracy with negligible increase in computational cost.