Convolution Neural Network Efficiency Research in Gender and Age Classification From Speech

Kuchebo, Anna V.; Bazanov, Vadim V.; Kondratev, Igor A.; Kataeva, Anastasia M.

doi:10.1109/elconrus51938.2021.9396365

Cited by 7 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the vector direction recommends the instantons variable. Unlike CNN, capsule network adapts dynamic routing model and abandon pooling layer rather interconnect capsule at distinct stages, hence accomplishing reliable and robust outcomes (Kuchebo et al, 2021). The procedure of dynamic routing attains the spatial relations among the entire and parts as well as routing the data among capsules by reinforcing the linking among capsules, thus capsules at distinct stages could accomplish higher reliability (Das et al, 2018).…”

Section: The Proposed Modelmentioning

confidence: 99%

“…The major benefit of this algorithm is it uses a library named OpenCV for image capturing that makes the execution simpler than one that uses neural network. In Kuchebo et al (2021), DL method explored the age and gender classifier problem on the basis of personal speech. The study performed research on the efficiency of the method presently utilized for identifying and verifying an individual by voice and investigated method of audio pre-processing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RETRACTED: Internet of agents system for age and gender classification using grasshopper optimization with deep convolution neural networks

et al. 2022

View full text Add to dashboard Cite

The internet of agents (IoA) is an emergent model, which mainly intends to resolve the limitations of the internet of things (IoT) devices with respect to reasoning and social competencies for improving proactivity, intelligence, and interoperability. This article presents a novel grasshopper optimization with deep learning enabled multi‐agent system for age and gender classification (GOADL‐MASAGC) model for IoA. The proposed GOADL‐MASAGC technique intends to categorize age as well as gender. The proposed GOADL‐MASAGC technique applies a multi‐agent system, which incorporates distinct processes for age and gender classification like pre‐processing, feature extraction, and classification. Besides, the GOADL‐MASAGC technique enables to performance concurrent process of classification and regression to identify age and gender respectively. In addition, the GOA with Capsule Network (CapsNet) model was executed for deriving a suitable group of feature vectors and the GOA is employed as a hyperparameter optimizer. Finally, wavelet kernel extreme learning machine (WKELM) was employed as a classifier for gender classification and deep belief network (DBN) is used as a regression approach for age recognition. For demonstrating the improved performance of the GOADL‐MASAGC model, a series of simulations were executed and the outcomes are examined in various aspects. The extensive comparative analysis reported the enhanced outcomes of the GOADL‐MASAGC approach over the existing methods.

show abstract

Section: The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: Internet of agents system for age and gender classification using grasshopper optimization with deep convolution neural networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Another work on age classification can be found in [35], which used a Convolutional neural network (CNN) to classify speech audio based on gender and age. They used multiple models in conjunction with each other for classification.…”

Section: Age Classificationmentioning

confidence: 99%

LimitAccess: on-device TinyML based robust speech recognition and age classification

Maayah

Abunada

Al-Janahi

et al. 2023

Discov Artif Intell

View full text Add to dashboard Cite

Automakers from Honda to Lamborghini are incorporating voice interaction technology into their vehicles to improve the user experience and offer value-added services. Speech recognition systems are a key component of smart cars, enhancing convenience and safety for drivers and passengers. In the future, safety-critical features may rely on speech recognition, but this raises concerns about children accessing such services. To address this issue, the LimitAccess system is proposed, which uses TinyML for age classification and helps parents limit children’s access to critical speech recognition services. This study employs a lite convolutional neural network (CNN) model for two different reasons: First, CNN showed superior accuracy compared to other audio classification models for age classification problems. Second, the lite model will be integrated into a microcontroller to meet its limited resource requirements. To train and evaluate our model, we created a dataset that included child and adult voices of the keyword “open”. The system approach categorizes voices into age groups (child, adult) and then utilizes that categorization to grant access to a car. The robustness of the model was enhanced by adding a new class (recordings) to the dataset, which enabled our system to detect replay and synthetic voice attacks. If an adult voice is detected, access to start the car will be granted. However, if a child’s voice or a recording is detected, the system will display a warning message that educates the child about the dangers and consequences of the improper use of a car. Arduino Nano 33 BLE sensing was our embedded device of choice for integrating our trained, optimized model. Our system achieved an overall F1 score of 87.7% and 85.89% accuracy. LimitAccess detected replay and synthetic voice attacks with an 88% F1 score.

show abstract

“…Ladde and Deshmukh (2015) have developed multiple classifier systems for emotion recognition and gender identification from the audios. CNN is used to classify gender and age from audio signals (Dat and The Anh 2019;Kuchebo et al 2021). Kattel et al (2019) presented chroma features extraction using different methods.…”

Section: Related Workmentioning

confidence: 99%

Automatic Grading of Student’s Presentation Skills based on PowerPoint Presentation and Audio

Borade

Kiwelekar

Netak

2022

UPjeng

View full text Add to dashboard Cite

Creating and delivering a presentation for Project, Seminar, and Course work is an important academic activity included in the curricula of undergraduate engineering studies. The presentation should be graded based on the presentation skills, accuracy, and authenticity of the contents covered in the presentation. Educational institutes use rubrics to assess the presentation skills on different grounds, which is a cumbersome task for the teacher when the strength of students is significant. Our main objective is to automatically grade the students' presentation skills in terms of PowerPoint presentations and the student's confidence. The proposed system describes a method and dataset designed to automate grading students' presentation skills. Our research study is divided into two parts. In the first part, the PowerPoint presentation features corresponding to text appearance, tables, charts, images, footer, and hyperlinks are extracted to grade PowerPoint presentations. At the same time, Mel-frequency Cepstral Coefficients, Mel Spectrogram, and Chroma features are extracted from the students' audio to identify confidence in the second part of the study. The audio is recorded at the presentation time. Feature extraction programs are implemented in python using Python-pptx and Librosa library. The tree-based feature selection method is used to remove the irrelevant features. Random Forest Ensemble model gives 100 % accuracy while predicting the grade of PowerPoint presentations. Multilayer Perceptron model gives 88% accuracy while predicting the confidence level of the students. The output of both models is combined to grade the students' presentation skills. The quadratic Weighted Kappa (QWK) score is 0.82, which indicates a significant similarity between automated and human-rated scores.

show abstract

Convolution Neural Network Efficiency Research in Gender and Age Classification From Speech

Cited by 7 publications

References 6 publications

RETRACTED: Internet of agents system for age and gender classification using grasshopper optimization with deep convolution neural networks

RETRACTED: Internet of agents system for age and gender classification using grasshopper optimization with deep convolution neural networks

LimitAccess: on-device TinyML based robust speech recognition and age classification

Automatic Grading of Student’s Presentation Skills based on PowerPoint Presentation and Audio

Contact Info

Product

Resources

About