Abstract
One of the most important factors affecting the performance of speech-based recognition systems is the differences between training and test conditions. The Nuisance attribute projection (NAP) is an effective method for eliminating these differences, called channel effects. In this study, the effects of the NAP approach in determining age and gender groups are investigated. Mel-frequency cepstral coefficients and delta coefficients are used as a feature and Gaussian mixture models (GMM) adapted from the universal background model by maximum-a-posteriori method are used for the modeling of age and gender classes. After the GMMs corresponding to each speech are converted into mean supervectors, they are applied to a Support Vector Machine (SVM), and speeches are classified according to the age and gender group of the speakers. While linear GMM kernel based on Kullback–Leibler divergence is used instead of standard SVM kernels, the NAP channel subspace size is changed between 20 and 200 and the number of GMM components is changed between 32 and 512 to determine the optimum values for these parameters. In the tests on the aGender database, the optimum number of components is determined as 128, and the optimum NAP channel subspace size is determined as 45. The age and gender classification accuracy of the system, which is developed using these optimum parameters, is increased from 60.52 to 62.03% with the use of NAP. In addition, age classification accuracy is increased from 60.23 to 61.82% and gender classification accuracy is increased from 91.71 to 92.30%.
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Abbreviations
- aGender:
-
Age and Gender Speech Corpus
- NAP:
-
Nuisance attribute projection
- MFCC:
-
Mel-frequency cepstral coefficient
- GMM:
-
Gaussian mixture model
- UBM:
-
Universal background model
- MAP:
-
Maximum-a-posteriori
- SVM:
-
Support vector machine
- C:
-
Child
- YF:
-
Young female
- YM:
-
Young male
- AF:
-
Adult female
- AM:
-
Adult male
- SF:
-
Senior female
- SM:
-
Senior male
- KL:
-
Kullback–Leibler
- HMM:
-
Hidden Markov Model
- DTW:
-
Dynamic time warping
- ANN:
-
Artificial neural network
- DNN:
-
Deep neural network
- SDC:
-
Shifted delta cepstral
- i-vector:
-
Identity vector
- P:
-
Positive
- N:
-
Negative
- TP:
-
True positive
- FN:
-
False negative
- TN:
-
True negative
- FP:
-
False positive
- utt a, utt b :
-
Utterance a and b
- HNR:
-
Harmonics-to-noise ratio
- PLP:
-
Perceptual linear prediction
- LPCC:
-
Linear prediction cepstrum coefficient
- \(D\) :
-
Feature size
- N:
-
Number of training points
- \({\alpha }_{i}\) :
-
Weights of the support vectors
- \({t}_{i}\) :
-
Ideal outputs
- \(K\left(x,{x}_{i}\right)\) :
-
Kernel function
- \({x}_{i}\) :
-
Support vectors
- \(x\) :
-
Observation
- \(d\) :
-
A learned constant
- \(b(x)\) :
-
A mapping
- \({\lambda }_{i}\) :
-
Mixture weights
- \(N()\) :
-
Gaussian function
- \({m}_{i}\) :
-
Mean vector
- \({\Sigma }_{i}\) :
-
Covariance matrix
- \(K\) :
-
The number of Gaussian components
- \({g}_{a}\) and \({g}_{b}\) :
-
GMM models for a and b utterance
- \(K\) :
-
NAP channel subspace size
- \(D({g}_{a}||{g}_{b})\) :
-
Natural distance between two utterances
- \({m}^{a}\), \({m}^{b}\) :
-
Mean supervisors for \(a\) and \(b\) utterance
- \({N}_{s}\) :
-
Number of speakers
- \({h}_{i}\) :
-
Number of sessions for the ith speaker
- \({\Phi }_{(1,{\mathrm{s}}_{1})}\) :
-
Expansion form of recordings of 1st speaker in 1st, session
- \({s}_{i}\) :
-
ith speaker
- \(I\) :
-
Identity matrix
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Yücesoy, E. Speaker age and gender classification using GMM supervector and NAP channel compensation method. J Ambient Intell Human Comput 13, 3633–3642 (2022). https://doi.org/10.1007/s12652-020-02045-4
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DOI: https://doi.org/10.1007/s12652-020-02045-4