Age Determination of Children in Preschool and Primary School Age with GMM-Based Supervectors and Support Vector Machines/Regression

Maier, Andreas; Nöth, Elmar

doi:10.1007/978-3-540-87391-4_33

Cited by 17 publications

(26 citation statements)

References 11 publications

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“…The method chosen for learning the prediction model was Support Vector Regression (SVR) [19] as used by previous authors [10,11,12,13]. The "e1071" package for the "R" statistics library was the chosen implementation [20].…”

Section: Machine Learningmentioning

confidence: 99%

A Comparison of Human and Machine Estimation of Speaker Age

Huckvale

Webb

2015

Statistical Language and Speech Processing

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Abstract. The estimation of the age of a speaker from his or her voice has both forensic and commercial applications. Previous studies have shown that human listeners are able to estimate the age of a speaker to within 10 years on average, while recent machine age estimation systems seem to show superior performance with average errors as low as 6 years. However the machine studies have used highly non-uniform test sets, for which knowledge of the age distribution offers considerable advantage to the system. In this study we compare human and machine performance on the same test data chosen to be uniformly distributed in age. We show that in this case human and machine accuracy is more similar with average errors of 9.8 and 8.6 years respectively, although if panels of listeners are consulted, human accuracy can be improved to a value closer to 7.5 years. Both human and machines have difficulty in accurately predicting the ages of older speakers.Keywords: speaker profiling, speaker age prediction, computational paralinguistics. IntroductionThe estimation of the age of a speaker from an analysis of his or her voice has forensic applications -for example the profiling of perpetrators of crimes [1], commercial applications -for example targeted advertising, and technological applicationsfor example adaptation of a speech recognition system to a speaker [2].Many previous studies have looked at the performance of both human listeners and machine-learning systems for the estimation of age from speech. Unfortunately, variations in the data set, task and performance metric make these studies hard to compare. In our work we take the view that the natural task should be numerical estimation of the age of the speaker, and the natural performance metric should be the mean absolute error (MAE) of estimation. The MAE answers the question "how close is the average estimate to the actual age?" A recent review of previous studies on human listener judgments of speaker age may be found in [3]. Of the studies reported which used numerical age estimation and

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Section: Machine Learningmentioning

confidence: 99%

A Comparison of Human and Machine Estimation of Speaker Age

Huckvale

Webb

2015

Statistical Language and Speech Processing

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“…This is motivated by the strong anatomic alteration during the process of growth [15]. We selected the different age classes according to automatic age recognition experiments described in [16]. The arrangement of the classes is motived by the fact, that children of an age of 9 and 10 could not be separated properly in terms of auto- [16].…”

Section: Age-dependent Acoustic Modelingmentioning

confidence: 99%

“…We selected the different age classes according to automatic age recognition experiments described in [16]. The arrangement of the classes is motived by the fact, that children of an age of 9 and 10 could not be separated properly in terms of auto- [16]. So the five age classes were defined to be < 7 years, 7 years, 8 years, 9 − 10 years and > 10 years.…”

Section: Age-dependent Acoustic Modelingmentioning

confidence: 99%

Improvement of a speech recognizer for standardized medical assessment of children's speech by integration of prior knowledge

Maier

Eysholdt²,

Nöth

2010

2010 IEEE Spoken Language Technology Workshop

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Speech recognition of children is a more difficult task than speech recognition of adults. This problem is amplified for children with articulation disorders like cleft lip and palate (CLP). In this work we improved our automatic speech recognition system by integrating prior knowledge. Prior knowledge focuses on two different aspects: A testdependent language modeling and an age-dependent acoustic modeling. These two approaches are merged at the end to different test-and age-dependent recognizers. We evaluated our system on a dataset of 35 children with CLP. Significant improvements could be found on this dataset. With our baseline system we achieved a negative word accuarcy (WA) of -11.0 %. By an extended language modeling we achieved 27.5 %. The age-dependent recognition system gains a huge improvement and achieves a WA of 42.6 %. With the significant improvements in WA it is possible to perform an automatic detection and identification of specific words. Thus, we took the first step towards a speech assessment on word and subword level.

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“…With the SVM, the weights of the network are found by solving a quadratic programming problem; the separation function between classes in SVM may be linear, or non-linear. (Bocklet, et al, 2008;Santosh, et al, 2012). The goal of SVM modelling is to find the optimal hyperplane that separates clusters of vector, (a set of features that describes one class), in such a way that the features that belong to the first class will be on one side of the plane and the features of the other class will be on the other side of the plane.…”

Section: The Gender Classifiermentioning

confidence: 99%

“…The goal of SVM modelling is to find the optimal hyperplane that separates clusters of vector, (a set of features that describes one class), in such a way that the features that belong to the first class will be on one side of the plane and the features of the other class will be on the other side of the plane. The data close to the hyper-plane are the support vectors (Bocklet, et al, 2008;Santosh, et al, 2012).…”

Section: The Gender Classifiermentioning

confidence: 99%

Objective Gender and Age Recognition from Speech Sentences

Faek

2015

ARO

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Abstract-In this work, an automatic gender and age recognizer from speech is investigated. The relevant features to gender recognition are selected from the first four formant frequencies and twelve MFCCs and feed the SVM classifier. While the relevant features to age has been used with k-NN classifier for the age recognizer model, using MATLAB as a simulation tool. A special selection of robust features is used in this work to improve the results of the gender and age classifiers based on the frequency range that the feature represents. The gender and age classification algorithms are evaluated using 114 (clean and noisy) speech samples uttered in Kurdish language. The model of two classes (adult males and adult females) gender recognition, reached 96% recognition accuracy. While for three categories classification (adult males, adult females, and children), the model achieved 94% recognition accuracy. For the age recognition model, seven groups according to their ages are categorized. The model performance after selecting the relevant features to age achieved 75.3%. For further improvement a denoising technique is used with the noisy speech signals, followed by selecting the proper features that are affected by the denoising process and result in 81.44% recognition accuracy.Index Terms-Age classification from speech, gender classification from speech, MFCC based gender and age recognition, SVM classifier.

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Age Determination of Children in Preschool and Primary School Age with GMM-Based Supervectors and Support Vector Machines/Regression

Cited by 17 publications

References 11 publications

A Comparison of Human and Machine Estimation of Speaker Age

A Comparison of Human and Machine Estimation of Speaker Age

Improvement of a speech recognizer for standardized medical assessment of children's speech by integration of prior knowledge

Objective Gender and Age Recognition from Speech Sentences

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