machine learning and its application to speech impediment therapy
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Project Data
Title: Machine Learning and its Application to Speech Impediment Therapy
Duration: February 2002 - February 2004www: <http://oasis.inf.u-szeged.hu/speechmaster>Grant No.: IKTA4-055/2001Keywords: artificial intelligence, machine learning, real-time phoneme recognition, teaching of reading, speech impediment therapy
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Project members:Co-ordinator:
University of Szeged,Department of InformaticsAddress: 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,
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SummaryThe 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.
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Machine Learning Algorithms
Basic assumptions:a) objects are characterized by featuresb) each feature constitutes one dimension in the feature space
Questions:- concept making- classification- feature selection- feature space transformation- dimension reduction
99%
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Linear feature space transformations:
- Principal Component Analysis
- Independent Component Analysis
- Linear Discriminant Analysis
- Springy Discriminant Analysis
Machine Learning Algorithms99%
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Machine Learning AlgorithmsNonlinear feature space transformations:
- Kernel Principal Component Analysis
- Kernel Independent Component Analysis
- Kernel Linear Discriminant Analysis
- Kernel Springy Discriminant Analysis
99%
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99% Machine Learning AlgorithmsMachine learning algorithms:
- Artificial Neural Nets
- Gaussian Mixture Modeling
- Support Vector Machines
- Projection Pursuit Learner
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a) real-time phoneme recognition
b) word recognition
Speech Recognition70%
Methods:
- machine learning
- speech corpora
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Speech Corpora
200 speakers of different ages
• 500 speakers(male/female 50-50%)
• age: 6-7
Speech impediment therapy Teaching of reading
90%
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The “SpeechMaster”60%
Phonological Awareness Teaching
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The “SpeechMaster”60%
Phoneme-Grapheme association
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The “SpeechMaster”60%
Visual feedback for the hearing impaired