Objective Gender and Age Recognition from Speech Sentences

Faek, Fatima K.

doi:10.14500/aro.10072

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…However, the recognition score remained low. The second issue is the design of a proper classification model [ 6 , 50 ]. Recently, deep learning models have been applied for age and gender recognition [ 7 ]; however, the aforementioned issues remain unresolved.…”

Section: Discussion and Comparative Analysismentioning

confidence: 99%

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

Tursunov

Mustaqeem

Choeh

et al. 2021

Sensors

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Speech signals are being used as a primary input source in human–computer interaction (HCI) to develop several applications, such as automatic speech recognition (ASR), speech emotion recognition (SER), gender, and age recognition. Classifying speakers according to their age and gender is a challenging task in speech processing owing to the disability of the current methods of extracting salient high-level speech features and classification models. To address these problems, we introduce a novel end-to-end age and gender recognition convolutional neural network (CNN) with a specially designed multi-attention module (MAM) from speech signals. Our proposed model uses MAM to extract spatial and temporal salient features from the input data effectively. The MAM mechanism uses a rectangular shape filter as a kernel in convolution layers and comprises two separate time and frequency attention mechanisms. The time attention branch learns to detect temporal cues, whereas the frequency attention module extracts the most relevant features to the target by focusing on the spatial frequency features. The combination of the two extracted spatial and temporal features complements one another and provide high performance in terms of age and gender classification. The proposed age and gender classification system was tested using the Common Voice and locally developed Korean speech recognition datasets. Our suggested model achieved 96%, 73%, and 76% accuracy scores for gender, age, and age-gender classification, respectively, using the Common Voice dataset. The Korean speech recognition dataset results were 97%, 97%, and 90% for gender, age, and age-gender recognition, respectively. The prediction performance of our proposed model, which was obtained in the experiments, demonstrated the superiority and robustness of the tasks regarding age, gender, and age-gender recognition from speech signals.

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Section: Discussion and Comparative Analysismentioning

confidence: 99%

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

Tursunov

Mustaqeem

Choeh

et al. 2021

Sensors

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“…For achieving the main two stages (features extracting and classification) some features extracting techniques must be used, such as [2][10] [11]: mel-scaled power spectrogram, melfrequency cepstral coefficient (MFCC), chorma-stft (short time fourier transform), spectral-contrast, tonnez, and linear predictive coding (LPC). As well as some classifiers such as [1][3] [12]: deep learning methods (including convolutional neural network (CNN) and probabilistic neural network (PNN)), eigenvoice (I-vector), gaussian matrix models (GMM), hidden markov model (HMM), support vector regression (SVR), multilayer perceptrons (MLP), k-nearest neighbor (K-NN), decision tree (DT), and bayes classifier.…”

Section: Speaker Age and Gender Recognitionmentioning

confidence: 99%

An overview for assessing a number of systems for estimating age and gender of speakers

Mohammed¹,

Al-Irhayim²

2022

Tikrit J. Pure Sci.

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The determination of the age and gender of the speaker of the speech signal is an interesting topic in the interaction between human-machine. Speech signal has a variety of applications ranging from speech analyses to allocate human-machine interactions. This paper aims to conduct a comparative study of age and gender classification algorithms applied to the speech signal. Comparison of experimental results of different sources of voices for speakers of different languages and methods of miscellaneous classification such as Bayes classifier, neural network, support vector machines, K-nearest neighbor, gaussien mixture model and hybrid method based on weighted analysis of a directed non-negative matrix and a neural network with a general recession as well as some deep learning methods, is done in order to show different results to classify the age and gender of the speaker when processing the speech signal. The study showed that methods and algorithms of deep learning have excelled in providing accuracy ratios higher than other methods, and it shows that the hybridization of two or more classification methods increases the accuracy level of the results.

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“…Исследования по совместному использованию в качестве признаков оценок частот формант и MFCC приведены в работе [7]. При этом точность правильного распознавания равнялась 94 %.…”

Section: литературный обзорunclassified

Automatic determination of a speaker’s gender based on the Cauchy distribution in the octave frequency band

Omelchenko¹

2019

ScienceRise

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Objective Gender and Age Recognition from Speech Sentences

Cited by 10 publications

References 12 publications

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms

An overview for assessing a number of systems for estimating age and gender of speakers

Automatic determination of a speaker’s gender based on the Cauchy distribution in the octave frequency band

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