Open AccessAnalysis of Spectral Features for Speaker Clustering
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i3027.0789s319
Subject(s) - centroid , feature (linguistics) , pattern recognition (psychology) , cluster analysis , speech recognition , speaker identification , spectral analysis , speaker recognition , spectral clustering , brightness , identification (biology) , computer science , artificial intelligence , mathematics , physics , linguistics , philosophy , botany , quantum mechanics , spectroscopy , optics , biology
In this paper Spectral feature like Spectral Roll off, Spectral Centroid, RMS (Root Mean Square) energy, Zero crossing Rate, Spectral irregularity, Brightness, of speech audio signals are extracted and analyzed. From analysis, prominent features are selected. These prominent features are used for speaker identification. For performing feature analysis, database of seven speakers is created. By using features, speakers are divided into two groups or clusters.