The measurement of the signal-to-noise ratio (SNR) in continuous speech
Speech Communication
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Automatic detection of pathologies in the voice by HOS based parameters
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
Discriminative feature weighting for HMM-based continuous speech recognizers
Speech Communication
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Principal Component Analysis of Spectral Perturbation Parameters for Voice Pathology Detection
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
An Alphanet approach to optimising input transformations for continuous speech recognition
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Computers in Biology and Medicine
EURASIP Journal on Applied Signal Processing
Pathological Voice Classification Based on a Single Vowel's Acoustic Features
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Automated speech analysis applied to laryngeal disease categorization
Computer Methods and Programs in Biomedicine
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
Laryngeal pathology detection by means of class-specific neural maps
IEEE Transactions on Information Technology in Biomedicine
Robust pathological voice detection based on component information from HMM
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Wavelet adaptation for automatic voice disorders sorting
Computers in Biology and Medicine
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This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.