Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks

Markitantov, Maxim; Verkholyak, Oxana

doi:10.1007/978-3-030-26061-3_34

Cited by 18 publications

(8 citation statements)

References 19 publications

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“…The proposed method provides the highest precision values for all datasets except DS 2 , and highest recall values for all datasets except DS 3 , and DS 4 . Considering all four performance metrics, it is observed that the next two best models followed the proposed model are Markitantov et al [30] and Ertam et al [33], which is also true in terms of wilcoxon Ranksum test. It is also observed that, the proposed method provides the highest values of all four performance measure metrics in case of dataset DS 1 , which contains the transgender speeches.…”

Section: Comparison Of Proposed Model With Other Related Modelsmentioning

confidence: 58%

“…For better visualization, the lists of values are represented by bar chart as shown in Figure 9. From the table and figure it is observed that, the proposed BF F SBR + CN N + GRU N model outperforms others in terms of accuracy and F-Measure in all datasets except dataset DS 3 where [30] shows the best F-Measure. The proposed method provides the highest precision values for all datasets except DS 2 , and highest recall values for all datasets except DS 3 , and DS 4 .…”

Section: Comparison Of Proposed Model With Other Related Modelsmentioning

confidence: 89%

“…The feature encoding process is designed to train the utterance-level classifier for better classification and a kernel extreme learning machine is further trained on the encoded vectors for better utterance-level classification. Markitantov et al [30] presented a novel approach in the paralinguistic field of age and gender recognition by speaker's voice based on deep neural networks. The training and testing of proposed models were implemented on the German speech corpus aGender.…”

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

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

Yasmin

Das

Nayak

et al. 2021

Preprint

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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.

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Section: Comparison Of Proposed Model With Other Related Modelsmentioning

confidence: 58%

Section: Comparison Of Proposed Model With Other Related Modelsmentioning

confidence: 89%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

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

Yasmin

Das

Nayak

et al. 2021

Preprint

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“…The same classes were considered in [36], where a system based on DNN was trained and tested. The obtained performance was 57.53% and 88.80% for age and gender classification, respectively.…”

Section: Introductionmentioning

confidence: 99%

Age group classification and gender recognition from speech with temporal convolutional neural networks

Sánchez-Hevia

Gil-Pita

Rosa-Zurera

2022

Multimed Tools Appl

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This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla’s Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%.

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“…Gender recognition studies have been made not only in the field of sound but also from the movements on the screen of touch screen phones and a success of 93.65% has been achieved [7]. In a study on gender and age estimation with fully-connected and convolutional neural networks using voice data collected from German speakers, age recognition rate was found to be 57.53%, and gender recognition rate was 88.8% [8]. Estimating the emotional state of the speakers is a very challenging task as it is influenced by many factors such as thought, mood, behavior and personality.…”

Section: Introductionmentioning

confidence: 99%

Gender Determination Using Voice Data

Taşpınar

Saritaş²,

Çinar

et al. 2020

International Journal of Applied Mathematics Electronics and Computers

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The rapid advancement of today's technologies, it is tried to facilitate whichever system will be used by using voice features such as person recognition and speech recognition by making use of the voices of the users. Organizations serving in these systems need less manpower and facilitate the operation by helping users faster. The decision-making process using sound features is a very challenging process. With gender recognition, which is one of these steps, it is possible to address the user by gender. In this study, it is aimed to define the genders according to the voices in terms of both forensic informatics and the rapid and accurate progress of the processes. In this study, 3168 male and female voice samples were taken as a dataset. Sound samples were first analyzed by acoustic analysis in R using seewave and tuneR packages. Artificial neural networks were used in the classification stage. In order to increase the classification accuracy, the dataset was divided into 10 parts and each part was excluded from training for testing and used for retesting. Average classification success was found by taking the arithmetic mean of the results. In the classification made with artificial neural networks, male and female voices could be distinguished from each other with a success of 97.9%.

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Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks

Cited by 18 publications

References 19 publications

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

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

Age group classification and gender recognition from speech with temporal convolutional neural networks

Gender Determination Using Voice Data

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