Automatic Speaker Age Estimation and Gender Dependent Emotion Recognition

Chaudhari, Shivaji J; Kagalkar, Ramesh M.

doi:10.5120/20644-3383

Cited by 14 publications

(14 citation statements)

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“…Te main principle of the gender identifcation system is to extract the features from the voice signals and provide the decision after comparing the extracted features with stored feature vectors. Te gender identifcation system has two phases: (1) the training phase and (2) the testing phase, as shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…Te research on the identifcation of gender has been rapidly analyzed in the recent era. Te basic and initial element of the voice processing system is the identifcation of gender [1]. Several approaches are compared to fnd the identifcation of the gender by using Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), and SVM with the analysis of the voice signals which are recorded via telephone channels [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

“…Te identifcation system is used to segregate the same group of speakers into two groups of gender, i.e., male and female [1]. Support vector machine (SVM) classifer can be used to predict, and the accuracy is achieved by more than 90% [21].…”

Section: Related Workmentioning

confidence: 99%

“…To reduce the noise from the noisy voice signals, voice signal enhancement techniques are applied. In the enhancement techniques, silence and noise are removed from the voice signal to enhance the quality of the voice signals [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers

Singhal

Sharma

2022

Journal of Engineering

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This paper presents the estimation of accuracy in male, female, and transgender identification using different classifiers with the help of voice signals. The recall value of each gender is also calculated. This paper reports the third gender (transgender) identification for the first time. Voice signals are the most appropriate and convenient way to transfer information between the subjects. Voice signal analysis is vital for accurate and fast identification of gender. The Mel Frequency Cepstral Coefficients (MFCCs) are used here as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature that configures the gender identification system. Recurrent Neural Network–Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) are utilized as classifiers in this work. In the proposed models, the experimental result does not depend on the text of the speech, the language of the speakers, and the time duration of the voice samples. The experimental results are obtained by analyzing the common voice samples. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a k-fold value of 5. The recall value of genders and accuracy of the proposed models also varied according to the number of voice samples in training and testing datasets. The highest accuracy for gender identification is found as 94.44%. The simulation results show that the accuracy of the RNN is always found at a higher value than SVM and LDA. The gender-wise highest recall value of the proposed model is 95.63%, 96.71%, and 97.22% for males, females, and transgender, respectively, using voice signals. The recall value of the transgender is high in comparison to other genders.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers

Singhal

Sharma

2022

Journal of Engineering

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“…However, no precise study between different age groups or their emotional states has been made by them. Use of melfrequency cepstral coefficient (MFCC) with different feature selection algorithm such as PCA (principle component analysis), supervised PCA (SPCA) has been attempted for different age groups in [6]. The prominent prosodic features representing speech emotion of children and adults could not be found in these literatures.…”

Section: This Work Was Not Supported By Any Organizationmentioning

confidence: 99%

Emotion Analysis from Speech of Different Age Groups

Palo¹,

Mohanty²,

Chandra³

2017

Annals of Computer Science and Information Systems

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Abstract-This Recognition of speech emotion based on suitable features provides age information that helps the society in different ways. As the length and shape of human vocal tract and vocal folds vary with age of the speaker, the area remains a challenge. Emotion recognition system based on speaker's age will help criminal investigators, psychologists and law enforcement agencies in dealing with different segments of the society. Particularly child psychologists, counselors can take timely preventive measures based on such recognition system. The area remains further complex since the recognition system trained for adult users performs poorer when it involves children. This has motivated the authors to move in this direction. A novel effort is made in this work to determine the age of speaker based on emotional speech prosody and clustering them using fuzzy c-means algorithm. The results are promising and we are able to demarcate the emotional utterances based on age.

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Estimation of Accuracy in Gender Identification Based on Voice Signals Using Different Classifiers

Singhal

Sharma

2023

Lecture Notes in Electrical Engineering

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Automatic Speaker Age Estimation and Gender Dependent Emotion Recognition

Abstract: Gender-dependent age, emotions (stress and feeling) are speaker qualities being examined in voice-based speaker voice processing system, these qualities

Cited by 14 publications

References 3 publications

Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers

Estimation of Accuracy in Human Gender Identification and Recall Values Based on Voice Signals Using Different Classifiers

Emotion Analysis from Speech of Different Age Groups

Estimation of Accuracy in Gender Identification Based on Voice Signals Using Different Classifiers

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