Analysis of Spectral Features for Speaker Clustering
Badhe Sanjay S1, Gulhane S. R2, Shirbahadurkar S. D3
1Mr. Badhe Sanjay S. , Research Scholar ( DYPIT), DYPCOE (SPPU), Pune, India.
2Mr. Gulhane S. R., Research Scholar ( DYPIT) , DYPCOE (SPPU), Pune, India.
3Dr. Shirbahadurkar S. D., Research Guide (DYPIT), Zeel COE (SPPU), Pune, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 16 July 2019 | Manuscript Published on 23 August 2019 | PP: 134-137 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I30270789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3027.0789S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Spectral Roll off, Spectral Centroid, RMS energy, Zero crossing Rate, Spectral irregularity & Brightness.
Scope of the Article: Clustering