1. Machine Learning and its Application to Speech Impediment Therapy

2. Project Data Title: Machine Learning and its Application to Speech Impediment Therapy Duration: February 2002 - February 2004 www: Grant No.: IKTA4-055/2001 Keywords: artificial intelligence, machine learning, real-time phoneme recognition, teaching of reading, speech impediment therapy

3. Project members: Co-ordinator: University of Szeged, Department of Informatics Address: H-6720, Szeged, Árpád tér 2. Project/team leader: András Kocsor Consortium members: University of Szeged, Training Teaching School, Team leader: János Bácsi, Kindergarten, Primary school and Boarding school, (the school for the deaf) Team leader: Jenő Mihalovics,

4. Summary The project consists of two main parts: a research part and an application part. The research part is devoted to investigating modern machine learning techniques which can form the basis of the development of several info- communication systems. The machine learning algorithms developed have been made available as open-source software. One example of these applications is speech recognition, which is the heart of the “SpeechMaster” software, developed in the second, application part of our project.

5. Machine Learning Algorithms Basic assumptions: a) objects are characterized by features b) each feature constitutes one dimension in the feature space Questions: - concept making - classification - feature selection - feature space transformation - dimension reduction 99%

6. Linear feature space transformations: - P rincipal C omponent A nalysis - I ndependent C omponent A nalysis - L inear D iscriminant A nalysis - S pringy D iscriminant A nalysis Machine Learning Algorithms 99%

7. Machine Learning Algorithms Nonlinear feature space transformations: - K ernel P rincipal C omponent A nalysis - K ernel I ndependent C omponent A nalysis - K ernel L inear D iscriminant A nalysis - K ernel S pringy D iscriminant A nalysis 99%

8. Machine Learning Algorithms Machine learning algorithms: - A rtificial N eural N ets - Gaussian Mixture Modeling - Support Vector Machines - Projection Pursuit Learner

9. a) real-time phoneme recognition b) word recognition Speech Recognition 70% Methods: - machine learning - speech corpora

10. Speech Corpora 200 speakers of different ages 500 speakers (male/female 50-50%) age: 6-7 Speech impediment therapy Teaching of reading 90%

11. The “SpeechMaster” 60% Phonological Awareness Teaching

12. The “SpeechMaster” 60% Phoneme-Grapheme association

13. The “SpeechMaster” 60% Visual feedback for the hearing impaired