关键词：
Feature extraction
Mel frequency cepstral coefficient
Machine learning
Neural networks
Perturbation methods
Complexity theory
Correlation
Automatic voice quality analysis
perceptual voice assessment
GRB scale
deep neural networks
摘要：
This article addresses the automatic assessment of voice quality according to the GRB scale, based on the use of a variety of deep learning architectures for prediction purposes. The proposed architectures are multimodal, because they employ multiples sources of information;and also multi-output, because they simultaneously predict all the traits of the GRB scale. A feature engineering approach is followed, based on the use of deep neural networks and a set of well-established features such as MFCC, perturbation and complexity characteristics. Likewise, a representation learning is considered, using convolutional neural networks feed on modulation spectra extracted from voices. Finally, diverse loss functions are also investigated, including two surrogate ordinal classification, a conventional weighed categorical cross-entropy, and a mean square error function. Experiments are carried out in a dataset containing registers of the sustained phonation of three vowels. The best deep learning architecture provides a relative performance improvement of 6.25% for G, 14.1% for R and 18.1% for B, in comparison with recently published results using the same dataset.
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中文科技期刊
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