machine learning and its application to speech impediment therapy

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

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,

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.

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%

Linear feature space transformations:

- Principal Component Analysis

- Independent Component Analysis

- Linear Discriminant Analysis

- Springy Discriminant Analysis

Machine Learning Algorithms99%

Machine Learning AlgorithmsNonlinear feature space transformations:

- Kernel Principal Component Analysis

- Kernel Independent Component Analysis

- Kernel Linear Discriminant Analysis

- Kernel Springy Discriminant Analysis

99%

99% Machine Learning AlgorithmsMachine learning algorithms:

- Artificial Neural Nets

- Gaussian Mixture Modeling

- Support Vector Machines

- Projection Pursuit Learner

a) real-time phoneme recognition

b) word recognition

Speech Recognition70%

Methods:

- machine learning

- speech corpora

Speech Corpora

200 speakers of different ages

• 500 speakers(male/female 50-50%)

• age: 6-7

Speech impediment therapy Teaching of reading

90%

The “SpeechMaster”60%

Phonological Awareness Teaching

The “SpeechMaster”60%

Phoneme-Grapheme association

The “SpeechMaster”60%

Visual feedback for the hearing impaired