Improved Gender Recognition System for Male and Female Speakers Using MFCC

2017 ◽  
Vol 15 (9) ◽  
pp. 23-28
Author(s):  
Jae-Seung Choi
Keyword(s):  
Recognition System ◽  
Gender Recognition ◽  
Male And Female

scholarly journals Coevolution of theS-Locus GenesSRK,SLGandSP11/SCRinBrassica oleraceaandB. rapa

Genetics ◽  
2002 ◽  
Vol 162 (2) ◽  
pp. 931-940 ◽  
Author(s):  
Keiichi Sato ◽  
Takeshi Nishio ◽  
Ryo Kimura ◽  
Makoto Kusaba ◽  
Tohru Suzuki ◽  
...  
Keyword(s):  
Brassica Oleracea ◽  
Gene Conversion ◽  
Phylogenetic Trees ◽  
Hypervariable Region ◽  
Recognition System ◽  
Self Incompatibility ◽  
Male And Female ◽  
Diversifying Selection ◽  
Tight Linkage ◽  
Nonsynonymous Site

AbstractBrassica self-incompatibility (SI) is controlled by SLG and SRK expressed in the stigma and by SP11/SCR expressed in the anther. We determined the sequences of the S domains of 36 SRK alleles, 13 SLG alleles, and 14 SP11 alleles from Brassica oleracea and B. rapa. We found three S haplotypes lacking SLG genes in B. rapa, confirming that SLG is not essential for the SI recognition system. Together with reported sequences, the nucleotide diversities per synonymous and nonsynonymous site (πS and πN) at the SRK, SLG, and SP11 loci within B. oleracea were computed. The ratios of πN:πS for SP11 and the hypervariable region of SRK were significantly >1, suggesting operation of diversifying selection to maintain the diversity of these regions. In the phylogenetic trees of 12 SP11 sequences and their linked SRK alleles, the tree topology was not significantly different between SP11 and SRK, suggesting a tight linkage of male and female SI determinants during the evolutionary course of these haplotypes. Genetic exchanges between SLG and SRK seem to be frequent; three such recent exchanges were detected. The evolution of S haplotypes and the effect of gene conversion on self-incompatibility are discussed.

scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

scholarly journals A rough set theory and deep learning based predictive system for gender recognition using audio speech

2021 ◽  
Author(s):  
Ghazaala Yasmin ◽  
ASIT KUMAR DAS ◽  
Janmenjoy Nayak ◽  
S Vimal ◽  
Soumi Dutta
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Set Theory ◽  
Rough Set ◽  
Gender Discrimination ◽  
Rough Set Theory ◽  
Recognition System ◽  
Gender Recognition ◽  
Learning Techniques

Abstract Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consists of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.

scholarly journals Database Creation and Dialect-Wise Comparative Analysis of Prosodic Features for Punjabi Language

2019 ◽  
Vol 29 (1) ◽  
pp. 1275-1282
Author(s):  
Shipra J. Arora ◽  
Rishipal Singh
Keyword(s):  
Comparative Analysis ◽  
Speech Recognition ◽  
Significant Variation ◽  
Recognition System ◽  
Speech Recognition System ◽  
Prosodic Features ◽  
Speech Corpus ◽  
Male And Female ◽  
Distinctive Features ◽  
Language Analysis

Abstract The paper represents a Punjabi corpus in the agriculture domain. There are various dialects in the Punjabi language and the main concentration is on major dialects, i.e. Majhi, Malwai and Doabi for the present study. A speech corpus of 125 isolated words is taken into consideration. These words are uttered by 100 speakers, i.e. 60 Malwi dialect speakers (30 male and 30 female), 20 Majhi dialect speakers (10 male and 10 female) and 20 Doabi dialect speakers (10 male and 10 female). Tonemes, adhak (geminated) and nasal words are selected from the corpus. Recordings have been processed through two mediums. The paper also elaborates some distinctive features of the corpus. This corpus is of quite significance for the speech recognition system. Prosodic characteristics such as intonation, rhythm and stress create a crucial impact on the speech recognition system. These characteristics vary from language to language as well as various dialects of a language. This paper portrays a comparative analysis of isolated words prosodic features of Malwi, Majhi and Doabi dialects of Punjabi language. Analysis is done using the PRAAT tool. Pitch, intensity, formant I and formant II values are extracted for toneme, adhak, nasal (bindi) and nasal (tippi) words. For all kinds of words, there is a significant variation in pitch (fundamental frequency), intensity, formant I and formant II values of male and female speakers of Malwi, Majhi and Doabi dialects. A detailed analysis has been discussed throughout this paper.

Generation of Synthetic Male and Female Walkers through Manipulation of a Biomechanical Invariant

Perception ◽  
1978 ◽  
Vol 7 (4) ◽  
pp. 393-405 ◽  
Author(s):  
James E Cutting
Keyword(s):  
Performance Level ◽  
Gender Recognition ◽  
Male And Female ◽  
Shoulder Movement ◽  
Males And Females ◽  
Point Light

Synthetic versions of human walkers were generated by computer as point-light displays. Previously it had been determined that the natural gaits of males and females differ according to the extent of movement at the shoulder and the hip. These movements were measured and then used to synthesize the stimuli used in the present study. These stimuli are shown here to be identified by untrained viewers as male when the shoulder movement is greater than the hip movement, and female when the configuration is reversed. Because of the coherence of the display lights representing the shoulder and hip are not necessary for gender recognition, although they do increase performance level. Hypernormality and heavy-footedness in gait are also discussed. Finally, all results are linked to an underlying biomechanical invariant, the center of moment.

An effective real time gender recognition system for smart cameras

2019 ◽  
Vol 11 (6) ◽  
pp. 2407-2419 ◽  
Author(s):  
Vincenzo Carletti ◽  
Antonio Greco ◽  
Alessia Saggese ◽  
Mario Vento
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Gender Recognition ◽  
Smart Cameras

A Novel Hybrid Metaheuristic Approach to Perceive Gender Based Identification System

2020 ◽  
Vol 13 ◽  
Author(s):  
Aparna Shukla ◽  
Suvendu Kanungo
Keyword(s):  
Search Space ◽  
Recognition System ◽  
High Dimensional ◽  
Identification System ◽  
Hybridization Technique ◽  
Optimization Approach ◽  
Gender Recognition ◽  
Accuracy Rate ◽  
Tree Classifier ◽  
Gender Based

Background: Gender recognition is one of the most challenging perceptible tasks that receiving attention in the increasing digital data era as the requirement of personalized, reliable and ethical system inevitable. A problem that we address in this paper, greatly deals with the gender based identification system. We are motivated by this problem as many recent social interactions and existing services rely on the gender of an individual, and also in forensic identification, the gender information provides the feasibility for easy and quick investigation. Objective: The paper primarily focused on the gender based identification problem and culminate a robust gender based recognition system with the higher accuracy rate. We attempted to perceive the gender of an individual through the multimodal biometric system by integrating the three prominent biometric traits namely: fingerprint, palm-print and hand in a specific manner. The proposed multimodal biometric for gender recognition system provides a better accuracy rate improvement with the optimal feature set which are generated from available high dimensional features set. Method: Aiming for the objective to reduce the search space, a hybrid meta-heuristic approach GSA-Firefly (GFF) is introduced in this paper. The optimization approach GFF is proposed to retrieve the optimal number of features from the high dimensional features generated by fusing the texture features of all the three considered biometric traits along with the fingerprint minutiae features. Further, the decision tree classifier is used to classify the gender of an individual. Results: The feasibility of the proposed approach is measured with different qualitative performance parameters. In light of achieving the accuracy rate of 99.2%, it shows that its performance comparatively better against other techniques reported in the literature with the different sets of classier. Conclusion: The hybridization technique that effectively integrate meta-heuristic approaches GSA and firefly outperforms other similar approaches with respect to obtaining the optimal features of multimodal biometric for gender based identification system. Further, the novel technique enhance the overall performance of the system by reducing the search space over time and space.

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