Digital spectral analysis: with applications
Digital spectral analysis: with applications
Machine Learning
Averaging regularized estimators
Neural Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Robustness to telephone handset distortion in speaker recognition by discriminative feature design
Speech Communication - Speaker recognition and its commercial and forensic applications
Subband architecture for automatic speaker recognition
Signal Processing - Special issue on emerging techniques for communication terminals
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Linear Prediction of Speech
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Audio-Visual Speaker Recognition for Video Broadcast News
Journal of VLSI Signal Processing Systems
Fusing Neural Networks Through Space Partitioning and Fuzzy Integration
Neural Processing Letters
Speaker-specific mapping for text-independent speaker recognition
Speech Communication
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Adaptive speaker identification with audiovisual cues for movie content analysis
Pattern Recognition Letters - Video computing
Multiple feature sets based categorization of laryngeal images
Computer Methods and Programs in Biomedicine
Towards a computer-aided diagnosis system for vocal cord diseases
Artificial Intelligence in Medicine
A genetic classification method for speaker recognition
Engineering Applications of Artificial Intelligence
Classifier combination based on confidence transformation
Pattern Recognition
Cepstrum-Based estimation of the harmonics-to-noise ratio for synthesized and human voice signals
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Laryngeal pathology detection by means of class-specific neural maps
IEEE Transactions on Information Technology in Biomedicine
Using the patient's questionnaire data to screen laryngeal disorders
Computers in Biology and Medicine
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders
Artificial Intelligence in Medicine
Selecting features from multiple feature sets for SVM committee-based screening of human larynx
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Random forests based monitoring of human larynx using questionnaire data
Expert Systems with Applications: An International Journal
Questionnaire- versus voice-based screening for laryngeal disorders
Expert Systems with Applications: An International Journal
Exploring similarity-based classification of larynx disorders from human voice
Speech Communication
Hierarchical ANN system for stuttering identification
Computer Speech and Language
Artificial Intelligence in Medicine
Hi-index | 0.00 |
The long-term goal of the work is a decision support system for diagnostics of laryngeal diseases. Colour images of vocal folds, a voice signal, and questionnaire data are the information sources to be used in the analysis. This paper is concerned with automated analysis of a voice signal applied to screening of laryngeal diseases. The effectiveness of 11 different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and two pathological classes, diffuse and nodular, is investigated. A k-NN classifier, SVM, and a committee build using various aggregation options are used for the classification. The study was made using the mixed gender database containing 312 voice recordings. The correct classification rate of 84.6% was achieved when using an SVM committee consisting of four members. The pitch and amplitude perturbation measures, cepstral energy features, autocorrelation features as well as linear prediction cosine transform coefficients were amongst the feature sets providing the best performance. In the case of two class classification, using recordings from 79 subjects representing the pathological and 69 the healthy class, the correct classification rate of 95.5% was obtained from a five member committee. Again the pitch and amplitude perturbation measures provided the best performance.