Study on the varying degree of speaker identity information reflected across the different MFCCs

Barlaskar, Saharul Alom; Laskar, Mohammad Azharuddin; Shome, Nirupam; Laskar, Rabul Hussain

doi:10.1109/inventive.2016.7823255

Cited by 4 publications

(1 citation statement)

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“…Generally, all speaker recognition systems contain four main modules: Speech Analysis, Feature Extraction, Speaker Model and Decision (Template Matching) [11].In this project voice recognition is working in 3 stages: Feature Extraction, Speaker Modeling and Verification.…”

Section: Voice Recognitionmentioning

confidence: 99%

Development of Academic Attendance System Using Voice Verification

Lazam

Saparon

2021

MJCS

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Based on the fact that all Human Voices are different, where every individual’s voice contains unique characteristics that can be distinguished from others by using special analysis, voice verification can also be used for biometric recognition of individual. The aim of this project is to develop an automated attendance process captivating and student databases record maintaining using C++ programming and voice verification technique. The Input speech will undergo a series of voice recognition processes. The process starts with Feature extraction to obtain the attribute of voice. The features or specific characteristics for each voice are extracted by linear predictive coding technique (LPC) into feature vectors which later be used in speaker modeling. In speaker modeling stage the feature vectors of each speaker will be then processed by Gaussian Mixture Modeling (GMM) technique to generate speaker models and will be stored in a database. Verification process is done through template matching technique. Based on log-likelihood logic decision (template matching technique) with respect to the FAR (False Acceptance Rate) and FRR (False Rejection Rate), the identity of that speaker is accepted if the match is above the threshold. Once the verification result is accepted, the attendance in MySQL database for that speaker will be auto-updated.

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Section: Voice Recognitionmentioning

confidence: 99%