Paper Info
Title
Speaker similarity score based fast phoneme classification by using neighborhood components analysis
Abstract
K-nearest neighbor (k-NN) classifier can learn non-linear decision surface and requires only one hyperparameter (i.e. value of “k”) for training. The classification accuracy improves as we increase the amount of training data. With an increase in the amount of training data, computational and memory requirements also increases as it has to store and search through the entire training data for classification of one test point. In this paper we investigate the computational and memory cost of speaker similarity score algorithm for phoneme classification by doing dimensionality reduction based on neighborhood components analysis. Our speaker similarity score algorithm uses k-NN for learning a speaker similarity score of a target speaker and uses this score for weighted k-NN phoneme classification. By using neighborhood components analysis we obtained a significant reduction in computational and memory requirements at the expense of a small phoneme classification performance loss. Experiments on TIMIT dataset shows 56% reduction in computational cost as we reduced the dimensions of our feature space from 50 to 22.
Year
DOI
Venue
2016
10.1109/GlobalSIP.2016.7905801
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Keywords
Field
DocType
Phoneme classification,speaker similarity,k-nearest neighbor,dimensionality reduction,feature learning,deep neural networks
TIMIT,Feature vector,Dimensionality reduction,Pattern recognition,Hyperparameter,Computer science,Speech recognition,Feature extraction,Memory management,Artificial intelligence,Classifier (linguistics),Decision boundary
Conference
ISSN
ISBN
Citations 
2376-4066
978-1-5090-4546-4
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Muhammad Rizwan1339.89
David V. Anderson241875.23