A new approach with score-level fusion for the classification of a speaker age and gender

Yücesoy, Ergün; Nabiyev, Vasif V.

doi:10.1016/j.compeleceng.2016.06.002

Cited by 22 publications

(17 citation statements)

References 16 publications

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“…The detailed accuracy for each class is depicted in Table 1. Table 2 shows the comparisons of our proposed voice recognition method with reported ones in [21][22][23][24][25][26]. The projected voice recognition method in this study has better than [21][22][23][24][25][26] with accuracy of about 100 %.…”

Section: Proposed Technique and Resultsmentioning

confidence: 85%

Gender voice classification with huge accuracy rate

Shareef

Jabar

Mezaal³

2020

TELKOMNIKA

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Gender voice recognition stands for an imperative research field in acoustics and speech processing as human voice shows very remarkable aspects. This study investigates speech signals to devise a gender classifier by speech analysis to forecast the gender of the speaker by investigating diverse parameters of the voice sample. A database has 2270 voice samples of celebrities, both male and female. Through Mel frequency cepstrum coefficient (MFCC), vector quantization (VQ), and machine learning algorithm (J 48), an accuracy of about 100% is achieved by the proposed classification technique based on data mining and Java script.

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Section: Proposed Technique and Resultsmentioning

confidence: 85%

Gender voice classification with huge accuracy rate

Shareef

Jabar

Mezaal³

2020

TELKOMNIKA

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“…e results were obtained on using the aGender dataset for the gender category with a 81.7% success rate. Yücesoy and Nabiyev [4] proposed a system for identifying speakers using a fusion score of seven subsystems where the feature vectors are the MFCC, PLP, and prosodic on three different classifiers that are GMM, SVM, and GMM-SV-based SVM combined at the score level. e classification success rate on gender identification using the aGender database is 90.4%.…”

Section: Introductionmentioning

confidence: 99%

DGR: Gender Recognition of Human Speech Using One-Dimensional Conventional Neural Network

Alkhawaldeh

2019

Scientific Programming

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The speech entailed in human voice comprises essentially paralinguistic information used in many voice-recognition applications. Gender voice is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e., male or female) from a voice signal. The contributions are three-fold including (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. The experimental results show the importance of subfeatures over others, which are vital for enhancing the efficiency of classification models’ performance. Experimentation reveals that the best recall value is equal to 99.97%; the best recall value is 99.7% for two models of deep learning (DL) and support vector machine (SVM), and with feature selection, the best recall value is 100% for SVM techniques.

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“…They managed to achieve a 96.4% success rate. The authors in [3] combined the estimated voice acoustic level of five different methods into one score level. The results were obtained on using aGender dataset for gender category of 81.7% success rates.…”

Section: Introductionmentioning

confidence: 99%

DGR: Deep Gender Recognition of Human Speech

Alkhawaldeh¹

2019

Preprint

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The speech entailed in human voice comprises essentially para-linguistic information used in many voice-recognition applications. Gender voice-recognition is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e, male or female) from a voice signal. The contributions are involved in two folds: (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. Experimental results show the importance of sub-features over others, which are vital for enhancing the efficiency of classification models performance. Experimentation reveals that the best recall value is equal to 99.97%; 99.7% of two models of Deep Learning (DL) and Support Vector Machine (SVM) and with feature selection the best recall value is 100% for SVM techniques.

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A new approach with score-level fusion for the classification of a speaker age and gender

Cited by 22 publications

References 16 publications

Gender voice classification with huge accuracy rate

Gender voice classification with huge accuracy rate

DGR: Gender Recognition of Human Speech Using One-Dimensional Conventional Neural Network

DGR: Deep Gender Recognition of Human Speech

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