Speaker recognition using spectral coefficients normalized with respect to unequal frequency bands

1997 ◽  
Vol 102 (3) ◽  
pp. 1285
Author(s):  
Mark Pawlewski
Keyword(s):  
Speaker Recognition ◽  
Frequency Bands ◽  
Spectral Coefficients ◽  
Unequal Frequency

Synthesis of Maitra cascades by means of spectral coefficients

1983 ◽  
Vol 130 (4) ◽  
pp. 101 ◽  
Author(s):  
M. Stanković ◽  
ž. Tošić ◽  
S. Nikolić
Keyword(s):  
Spectral Coefficients

Decomposition of Fluctuating Photovoltaic Generation Power in Frequency Bands and Analysis of Chaotic Properties

2016 ◽  
Vol 136 (7) ◽  
pp. 621-627
Author(s):  
Akiko Takahashi ◽  
Akihiro Yamagata ◽  
Jun Imai ◽  
Shigeyuki Funabiki
Keyword(s):  
Photovoltaic Generation ◽  
Frequency Bands ◽  
Generation Power

Model for Estimating Effects of Human Body Shadowing in High Frequency Bands

2015 ◽  
Vol E98.B (5) ◽  
pp. 773-782 ◽  
Author(s):  
Ngochao TRAN ◽  
Tetsuro IMAI ◽  
Yukihiko OKUMURA
Keyword(s):  
Human Body ◽  
High Frequency ◽  
Frequency Bands

A Digital Correlation System for Preliminary Signal Processing in Multiplexed Frequency Bands

1997 ◽  
Vol 51 (6-7) ◽  
pp. 25-27
Author(s):  
V. N. Frankov ◽  
G. Y. Osokin ◽  
O. V. Gavrentiuk ◽  
A. I. Samokhvalov
Keyword(s):  
Signal Processing ◽  
Frequency Bands ◽  
Digital Correlation ◽  
Correlation System

Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

2020 ◽  
Vol 64 (4) ◽  
pp. 40404-1-40404-16
Author(s):  
I.-J. Ding ◽  
C.-M. Ruan
Keyword(s):  
Face Detection ◽  
Speaker Recognition ◽  
Visual Information ◽  
Classification Tree ◽  
Gaussian Mixture ◽  
Recognition Method ◽  
Indoor Space ◽  
Identity Recognition ◽  
Visual Identity ◽  
Speaker Classification

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

New Feature Vectors using GFCC for Speaker Identification

2018 ◽  
Vol 6 (8) ◽  
pp. 243
Author(s):  
A. Nagesh
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Extraction Methods ◽  
Identification System ◽  
Identification Performance ◽  
Feature Vectors ◽  
Overall Performance ◽  
New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system.  The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is  GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

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