Age estimation based on speech features and support vector machine

Mahmoodi, Davood; Marvi, Hossein; Taghizadeh, Mehdi; Soleimani, Ali; Razzazi, Farbod; Mahmoodi, Marzieh

doi:10.1109/ceec.2011.5995826

Cited by 24 publications

(18 citation statements)

References 9 publications

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“…During this decade, many different techniques, mostly inspired from the automatic speaker and language recognition fields, have been suggested for categorizing speakers based on their age groups. For example, using different types of acoustic features and Support Vector Machines (SVM) (Mahmoodi et al, 2011;Chen et al, 2011;van Heerden et al, 2010), Gaussian Mixture Model (GMM) mean supervectors and SVM (Bocklet et al, 2008a), nuisance attribute projection (Dobry et al, 2009), anchor models (Dobry et al, 2009) and parallel phoneme recognizers (Metze et al, 2007). The age sub-challenge of the Interspeech 2010 paralinguistic challenge provided a forum for presenting stateof-the-art methods in speaker age group classification (Schuller et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Speaker age estimation using i-vectors

Bahari

McLaren

hamme

et al. 2014

Engineering Applications of Artificial Intelligence

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In this paper, a new approach for age estimation from speech signals based on i-vectors is proposed. In this method, each utterance is modeled by its corresponding i-vector. Then, a Within-Class Covariance Normalization technique is used for session variability compensation. Finally, a least squares support vector regression (LSSVR) is applied to estimate the age of speakers. The proposed method is trained and tested on telephone conversations of the National Institute for Standard and Technology (NIST) 2010 and 2008 speaker recognition evaluation databases. Evaluation results show that the proposed method yields significantly lower mean absolute error and higher Pearson correlation coefficient between chronological speaker age and estimated speaker age compared to different conventional schemes. The obtained relative improvements of mean absolute error and correlation coefficient compared to our best baseline system are around 5% and 2% respectively. Finally, the

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Section: Related Workmentioning

confidence: 99%

Speaker age estimation using i-vectors

Bahari

McLaren

hamme

et al. 2014

Engineering Applications of Artificial Intelligence

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“…Many approaches to speaker's age estimation were proposed in the literature. Most of them are based on mel frequency cepstral coefficients (MFCC) as the voice signal features [4][5][6][7][8][9][10]. In study [11], formants and harmonics were used.…”

Section: Introductionmentioning

confidence: 99%

“…In study [11], formants and harmonics were used. For age classification, the most popular approaches employ support vector machine and its modifications [4,5,7,9,10,[12][13][14]. The Gaussian mixture models [5,6,8,9,[12][13][14][15] and hidden Markov models were also often used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Adolescent age estimation using voice features

Bugdoł

Bugdol

Bieńkowska

et al. 2020

Biomedical Engineering / Biomedizinische Technik

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In this paper, a method for evaluating the chronological age of adolescents on the basis of their voice signal is presented. For every examined child, the vowels a, e, i, o and u were recorded in extended phonation. Sixty voice parameters were extracted from each recording. Voice recordings were supplemented with height measurement in order to check if it could improve the accuracy of the proposed solution. Predictor selection was performed using the LASSO (least absolute shrinkage and selection operator) algorithm. For age estimation, the random forest (RF) for regression method was employed and it was tested using a 10-fold cross-validation. The lowest absolute error (0.37 year ± 0.28) was obtained for boys only when all selected features were included into prediction. In all cases, the achieved accuracy was higher for boys than for girls, which results from the fact that the change of voice with age is larger for men than for women. The achieved results suggest that the presented approach can be employed for accurate age estimation during rapid development in children.

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“…Based on different acoustical features and classifiers, a large number of methods for evaluation of speaker's age have been proposed in literature [ 2 , 9 , 10 ]. Common features of such systems include using hidden Markov models (HMM) [ 11 ], support vector machines [ 12 – 14 ], and Gaussian mixture model (GMM) [ 2 ] and improvement of the age classes based on data projection to lower spaces [ 1 , 15 ]. Iseli et al [ 1 ] modeled speakers by HMM weight supervector.…”

Section: Introductionmentioning

confidence: 99%

“…Using SVM to classify the features, they reported up to 10% improvement of the accuracy via the proposed dimension reduction. Mahmoodi et al [ 12 ] used an SVM with RBF kernel, which received Mel-frequency cepstral coefficients (MFCC) and PLP coefficients as features. They repeated the experiments for different numbers of MFCCs.…”

Section: Introductionmentioning

confidence: 99%

Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy

Mirhassani

Zourmand

Ting

2014

The Scientific World Journal

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Automatic estimation of a speaker's age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker's age is presented. The method features a “divide and conquer” strategy wherein the speech data are divided into six groups based on the vowel classes. There are two reasons behind this strategy. First, reduction in the complicated distribution of the processing data improves the classifier's learning performance. Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. Subsequently, fuzzy data fusion is employed to provide an overall decision by aggregating the classifier's outputs. The results are then compared with a number of state-of-the-art age estimation methods. Experiments conducted based on six age groups including children aged between 7 and 12 years revealed that fuzzy fusion of the classifier's outputs resulted in considerable improvement of up to 53.33% in age estimation accuracy. Moreover, the fuzzy fusion of decisions aggregated the complementary information of a speaker's age from various speech sources.

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Age estimation based on speech features and support vector machine

Cited by 24 publications

References 9 publications

Speaker age estimation using i-vectors

Speaker age estimation using i-vectors

Adolescent age estimation using voice features

Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy

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