Open AccessRobust Speech Hash Function
Author(s) -
Chen Ning,
Wan Wanggen
Publication year - 2010
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.10.0209.0309
Subject(s) - hash function , non negative matrix factorization , robustness (evolution) , speech recognition , computer science , speech processing , linear prediction , pattern recognition (psychology) , matrix decomposition , artificial intelligence , biochemistry , eigenvalues and eigenvectors , physics , chemistry , computer security , quantum mechanics , gene
In this letter, we present a new speech hash function based on the non‐negative matrix factorization (NMF) of linear prediction coefficients (LPCs). First, linear prediction analysis is applied to the speech to obtain its LPCs, which represent the frequency shaping attributes of the vocal tract. Then, the NMF is performed on the LPCs to capture the speech's local feature, which is then used for hash vector generation. Experimental results demonstrate the effectiveness of the proposed hash function in terms of discrimination and robustness against various types of content preserving signal processing manipulations.