Phonological features in discriminative classification of dysarthric speech
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Alternative speech communication system for persons with severe speech disorders
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
A log-linearized Gaussian mixture network and its application toEEG pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
| Hi-index | 0.00 |
We introduce a new framework to improve the dysarthric speech recognition by using the rhythm knowledge. This approach builds speaker-dependent (SD) recognizers with respect to the dysarthria severity level of each speaker. This severity level is determined by a hybrid classifier combining class posterior distributions and a hierarchical structure of multilayer perceptrons. To perform this classification, rhythm-based features are used as input parameters since the preliminary evidence from perceptual experiments shows that rhythm troubles may be the common characteristic of various types of dysarthria. Then, a speaker-dependent dysarthric speech recognition is performed by using Hidden Markov Models (HMMs). The Nemours database of American dysarthric speakers is used throughout experiments. Results show the relevance of rhythm metrics and the effectiveness of the proposed framework to improve the performance of dysarthric speech recognition.