Corpus ID: 53736488

Robustness against the channel effect in pathological voice detection

  title={Robustness against the channel effect in pathological voice detection},
  author={Yi-Te Hsu and Zining Zhu and Chi-Te Wang and Shih-Hau Fang and F. Rudzicz and Yu Tsao},
Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect is a pervasive problem in machine learning for healthcare. In this study, we propose a detection system for pathological voice, which is robust against the channel effect. This… Expand
2 Citations
Combining acoustic signals and medical records to improve pathological voice classification
Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall (UAR) improvements of 2.02–10.32% and 2.48–17.31%, respectively, compared with systems that use only acoustic signals or medical records. Expand
Talk2Me: Automated linguistic data collection for personal assessment
A web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks, and a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features, which is the most comprehensive publicly available software for extracting linguistic features. Expand


Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.
By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Expand
Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters
Using the F-Ratio and Fisher's discriminant ratio, it will be demonstrated that the detection of voice impairments can be performed using both mel cepstral vectors and their first derivative, ignoring the second derivative. Expand
Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework
A voice pathology detection system using deep learning on the mobile healthcare framework using the existing robust CNN models and the VGG-16 and CaffeNet models are investigated in the paper. Expand
A Deep Learning Method for Pathological Voice Detection Using Convolutional Deep Belief Networks
A novel system for pathological voice detection using convolutional neural network (CNN) as the basic architecture is presented and it will be shown that a small amount of data can be used to achieve good results in classification with this deep learning approach. Expand
Discrimination of pathological voices using a time-frequency approach
A joint time-frequency approach for classifying pathological voices using continuous speech signals that obviates the need for segmentation and promotes accurate prediction of abnormal voice quality that is relevant to the client's "real world" experience. Expand
Pathological voice discrimination using cepstral analysis, vector quantization and Hidden Markov Models
Results obtained show an effective and objective way in analyzing voice disorders caused by a vocal fold pathology in patients affected by vocal fold pathologies. Expand
Automatic Detection of Laryngeal Pathologies in Records of Sustained Vowels by Means of Mel-Frequency Cepstral Coefficient Parameters and Differentiation of Patients by Sex
It is shown that the automatic detection of laryngeal pathology on voice records based on MFCC can significantly improve its performance by means of this prior differentiation by sex. Expand
Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients
A strategy based on combining classifiers for fusing the nonlinear analysis with the information provided by classic parameterization approaches found in the literature, and the best accuracy obtained is 98.23% ± 0.001. Expand
Voice Disorders Identification Using Multilayer Neural Network
A new method for voice disorders classification based on multilayer neural network based on a hybrid technique which uses the wavelets energy coefficients as input of the multilayers neural network is presented. Expand
Automatic speech recognition for acoustical analysis and assessment of cantonese pathological voice and speech
  • Tan Lee, Yuanyuan Liu, +7 authors S. Law
  • Computer Science
  • 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2016
The results confirm the feasibility and potential of using natural speech for acoustical assessment of voice and speech disorders, and reveal the challenging issues in acoustic modeling and language modeling of pathological speech. Expand