User voice identification on FPGA

2005 ◽  
Author(s):  
J. Xu ◽  
A. Ariyaeeinia ◽  
R. Sotudeh
Keyword(s):  
Voice Identification

Voice identification through spectral analysis

2006 ◽  
Vol 9 (6) ◽  
pp. 52-55 ◽  
Author(s):  
Engelberg ◽  
Saidoff ◽  
Israeli
Keyword(s):  
Spectral Analysis ◽  
Voice Identification

scholarly journals Two-component network model in voice identification technologies

2018 ◽  
Vol 25 (1) ◽  
pp. 81-89 ◽  
Author(s):  
Edita K. Kuular ◽  
Andrey I. Trufanov ◽  
Alexei A. Tikhomirov
Keyword(s):  
Network Model ◽  
Voice Identification ◽  
Two Component

scholarly journals Voice Identification Using MFCC and Vector Quantization

2020 ◽  
Vol 17 (3(Suppl.)) ◽  
pp. 1019
Author(s):  
Bassel Alkhatib ◽  
Mohammad Madian Waleed Kamal Eddin
Keyword(s):  
Vector Quantization ◽  
Speech Processing ◽  
Large Scale ◽  
Feature Matching ◽  
Speaker Identification ◽  
Voice Recognition ◽  
Security Systems ◽  
Voice Identification ◽  
New Methodologies ◽  
Quick Search

The speaker identification is one of the fundamental problems in speech processing and voice modeling. The speaker identification applications include authentication in critical security systems and the accuracy of the selection. Large-scale voice recognition applications are a major challenge. Quick search in the speaker database requires fast, modern techniques and relies on artificial intelligence to achieve the desired results from the system. Many efforts are made to achieve this through the establishment of variable-based systems and the development of new methodologies for speaker identification. Speaker identification is the process of recognizing who is speaking using the characteristics extracted from the speech's waves like pitch, tone, and frequency. The speaker's models are created and saved in the system environment and used to verify the identity required by people accessing the systems, which allows access to various services that are controlled by voice, speaker identification involves two main parts: the first part is the feature extraction and the second part is the feature matching.

scholarly journals Evaluating Earwitness Identification Procedures: The Effect of Pre-Parade Instructions

2020 ◽  
Author(s):  
Harriet M J Smith ◽  
JENS ROESER ◽  
Nikolas Pautz ◽  
Josh P Davis ◽  
Jeremy Robson ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
False Alarms ◽  
Wrongful Conviction ◽  
Voice Identification ◽  
Sequential Procedures ◽  
Testing Performance ◽  
Low Performance ◽  
Shed Light ◽  
Present Standard ◽  
Identification Procedures

Voice identification parades can be unreliable, as earwitness responses are error-prone. Here we vary pre-parade instructions, testing performance across serial and sequential procedures to examine ways of reducing errors. The participants listened to a target voice and later attempted to identify it from a parade. They were either warned that the target may or may not be present (standard warning), or encouraged to consider responding ‘not present’ because of the associated risk of a wrongful conviction (strong warning). Overall accuracy was low. Performance varied according to instructions and procedure. False alarms were lower on target-absent serial parades following the strong compared to the standard warning. However, the strong warning was associated with higher false alarms on target-absent sequential parades. We discuss the cognitive processes that might drive this effect. Our novel analyses shed light on these results, highlighting the challenges of directly comparing procedures, and revealing position-related effects.

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