Estimating Age in Short Utterances Based on Multi-Class Classification Approach

Badr, Ameer; Abdul-Hassan, Alia K.

doi:10.32604/cmc.2021.016732

Cited by 7 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These criteria are determined in advance, so, there are two types of accuracy evaluation metrics, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are used. In this study, the standards were tested on the news classification of the speaker (female/male) and will be described in detail [1,[93][94][95].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…With the world's latest tremendous development, technological advancement, and information age, we started accessing this study which is referred to the main terms in the study, i.e., each of Multimodal Age Estimation [1,2]. Typically, it is more challenging to assume the age of a speaker based on their speech [3], Gender Estimation [4], and Human-Robot Interaction [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Et.al. [22,23] used the accumulated statistic of MFCC and LPC with their first and second derivatives, Spectral Sub-band Coefficients (SSC), as well as the first 4 formants (f1-f4), and MAE of 10.3 and 9.25 years on the VoxCeleb dataset, and 7.73 and 4.96 for male and female age on the TIMIT dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Finding the ideal feature set to represent various bodily features, nevertheless, is challenging. In the research on speaker profiling, several feature selection and dimensionality reduction techniques have been used for age feature selection including Catbthe oost optimization technique [23], Principle Component Analysis (PCA) [3], and Linear Discriminant Analysis (LDA) [22,24,25].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Feature Selection for Age Estimation from Speech

2023

IJCCCE

View full text Add to dashboard Cite

The voice signal carries a wide range of data about the speaker, including their physical characteristics, feelings, and level of health. There are several uses for the estimate of these physical characteristics from the speech in forensics, security, surveillance, marketing, and customer service. The primary goal of this research is to identify the auditory characteristics that aid in estimating a speaker’s age. To this end, an ensemble feature selection model is proposed that selects the best features from a baseline acoustic feature vector for age estimation from speech. Using a feature vector that covers various spectral, temporal, and prosodic aspects of speech, an ensemble-based automatic feature selection is performed by, first calculating the feature importance or ranks based on individual feature selection methods, then voting is applied to the resulting feature ranks to attain the top-ranked subset by all feature selection methods. The proposed method is evaluated on the TIMIT dataset and achieved a mean absolute error (MAE) of 5.58 years and 5.12 years for male and female age estimation. Index Terms— Age Estimation, Feature Selection, Ensemble Selection, TIMIT dataset.

show abstract

“…Another factor that affects the performance of ASP is the high dimensionality of features. Dimensionality reduction (such as principle component analysis and LDA), and feature selection algorithms employed in several studies, such as [35,46,57], reported an increase in performance and reduction in computation time.…”

Section: (A) (B)mentioning

confidence: 99%

Review of Automatic Speaker Profiling: Features, Methods, and Challenges

Jaid,

Abdul Hassan

2023

Iraqi Journal of Science

View full text Add to dashboard Cite

Automatic Speaker Profiling (ASP), is concerned with estimating the physical traits of a person from their voice. These traits include gender, age, ethnicity, and physical parameters. Reliable ASP has a wide range of applications such as mobile shopping, customer service, robotics, forensics, security, and surveillance systems. Research in ASP has gained interest in the last decade, however, it was focused on different tasks individually, such as age, height, or gender. In this work, a review of existing studies on different tasks of speaker profiling is performed. These tasks include age estimation and classification, gender detection, height, and weight estimation This study aims to provide insight into the work of ASP, available datasets, feature extraction techniques, and learning models. Further, the performance of current speaker profiling systems is investigated. Finally, the challenges of speaker profiling are presented at the end of this review.

show abstract

Estimating Age in Short Utterances Based on Multi-Class Classification Approach

Cited by 7 publications

References 12 publications

Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review

Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review

Ensemble Feature Selection for Age Estimation from Speech

Review of Automatic Speaker Profiling: Features, Methods, and Challenges

Contact Info

Product

Resources

About