Noise Robust Speaker Identification Using RASTA–MFCC Feature with Quadrilateral Filter Bank Structure

2016 ◽  
Vol 91 (3) ◽  
pp. 1321-1333 ◽  
Author(s):  
S. Selva Nidhyananthan ◽  
R. Shantha Selva Kumari ◽  
T. Senthur Selvi
Keyword(s):  
Filter Bank ◽  
Speaker Identification ◽  
Bank Structure ◽  
Robust Speaker Identification ◽  
Noise Robust

scholarly journals Performance Evaluation of Mel and Bark Scale based Features for Text-Independent Speaker Identification

2019 ◽  
Vol 8 (11) ◽  
pp. 3734-3738
Keyword(s):  
Speaker Recognition ◽  
Filter Bank ◽  
Work Performance ◽  
Speaker Identification ◽  
Recognition Rate ◽  
Identification System ◽  
Bank Structure ◽  
Speech Features ◽  
Recognition Systems ◽  
Human Ear

The performance of Mel scale and Bark scale is evaluated for text-independent speaker identification system. Mel scale and Bark scale are designed according to human auditory system. The filter bank structure is defined using Mel and Bark scales for speech and speaker recognition systems to extract speaker specific speech features. In this work, performance of Mel scale and Bark scale is evaluated for text-independent speaker identification system. It is found that Bark scale centre frequencies are more effective than Mel scale centre frequencies in case of Indian dialect speaker databases. Mel scale is defined as per interpretation of pitch by human ear and Bark scale is based on critical band selectivity at which loudness becomes significantly different. The recognition rate achieved using Bark scale filter bank is 96% for AISSMSIOIT database and 95% for Marathi database.

DNN-Based Amplitude and Phase Feature Enhancement for Noise Robust Speaker Identification

2016 ◽  
Author(s):  
Zeyan Oo ◽  
Yuta Kawakami ◽  
Longbiao Wang ◽  
Seiichi Nakagawa ◽  
Xiong Xiao ◽  
...  
Keyword(s):  
Speaker Identification ◽  
Feature Enhancement ◽  
Robust Speaker Identification ◽  
Noise Robust
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