Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Adaptive phoneme alignment based on rough set theory
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Classification of speech dysfluencies with MFCC and LPCC features
Expert Systems with Applications: An International Journal
Acoustic transformations to improve the intelligibility of dysarthric speech
SLPAT '11 Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies
Classification of Speech Dysfluencies Using LPC Based Parameterization Techniques
Journal of Medical Systems
Hierarchical ANN system for stuttering identification
Computer Speech and Language
Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
Digital Signal Processing
| Hi-index | 0.00 |
The process of counting stuttering events could be carried out more objectively through the automatic detection of stop-gaps, syllable repetitions and vowel prolongations. The alternative would be based on the subjective evaluations of speech fluency and may be dependent on a subjective evaluation method. Meanwhile, the automatic detection of intervocalic intervals, stop-gaps, voice onset time and vowel durations may depend on the speaker and the rules derived for a single speaker might be unreliable when trying to consider them as universal ones. This implies that learning algorithms having strong generalization capabilities could be applied to solve the problem. Nevertheless, such a system requires vectors of parameters, which characterize the distinctive features in a subject's speech patterns. In addition, an appropriate selection of the parameters and feature vectors while learning may augment the performance of an automatic detection system.The paper reports on automatic recognition of stuttered speech in normal and frequency altered feedback speech. It presents several methods of analyzing stuttered speech and describes attempts to establish those parameters that represent stuttering event. It also reports results of some experiments on automatic detection of speech disorder events that were based on both rough sets and artificial neural networks.