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Machine Learning and its Application to Speech Impediment Therapy.

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Presentation on theme: "Machine Learning and its Application to Speech Impediment Therapy."— Presentation transcript:

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 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


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