Advanced time‐frequency displays applied to forensic speaker identification.

2009 ◽  
Vol 125 (4) ◽  
pp. 2530-2530
Author(s):  
Sean A. Fulop ◽  
Sandra Ferrari Disner
Keyword(s):  
Speaker Identification ◽  
Time Frequency ◽  
Forensic Speaker Identification

Historical, current, and legal bases for forensic speaker identification

2016 ◽  
Vol 140 (4) ◽  
pp. 3228-3228
Author(s):  
Al Yonovitz ◽  
Herbert Joe ◽  
Joshua Yonovitz
Keyword(s):  
Speaker Identification ◽  
Forensic Speaker Identification

scholarly journals Speaker Identification using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 1216-1218
Keyword(s):  
Deep Learning ◽  
Speaker Identification ◽  
Frequency Assignment ◽  
Learning Approaches ◽  
Speech Separation ◽  
Rest Point ◽  
Time Frequency ◽  
Learning Framework ◽  
Frequency Representation ◽  
Permutation Problem

Whatever the modern achievement of deep learning for several terminology processing tasks, single-microphone, speaker-independent speech separation remains difficult for just two main things. The rest point is that the arbitrary arrangement of the goal and masker speakers in the combination (permutation problem), and also the following is the unidentified amount of speakers in the mix (output issue). We suggest a publication profound learning framework for speech modification, which handles both issues. We work with a neural network to project the specific time-frequency representation with the mixed-signal to a high-dimensional categorizing region. The time-frequency embeddings of the speaker have then made to an audience around corresponding attractor stage that is employed to figure out the time-frequency assignment with this speaker identifying a speaker using a blend of speakers together with the aid of neural networks employing deep learning. The purpose function for your machine is standard sign renovation error that allows finishing functioning throughout both evaluation and training periods. We assessed our system with all the voices of users three and two speaker mixes and also document similar or greater performance when compared with another advanced level, deep learning approaches for speech separation.

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