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Biologically inspired statistical methods for flexible automatic speech understanding (»Add to Infobox)

Research Leader: Giampiero Salvi


Speech Communication and Technology

The project will develop machine learning methods for speech understanding that more closely resemble the biological approach to learning. It will contribute to more flexible speech interfaces and also to basic research in cognitive science on modelling language acquisition in humans.
The project will develop machine learning methods for speech understanding that more closely resemble the biological approach to learning. This in order to introduce more flexibility in the speech understanding systems. The proposed methods will allow the system to estimate if a new input does not agree with its current state of knowledge. When this happens, the system will be able to modify its state of knowledge and include the new input.

This requires the ability to judge the current model on the basis of new observations, and the ability to learn the new input in a semi-supervised way from the context. For this purpose we will use advanced machine learning techniques, including unsupervised and semi-supervised methods. The methods will be tested on multi-modal speech databases and in a robotic platform that interacts with its environment as well as the conversational partner.

Previously, we have conducted studies on applying unsupervised methods to speech processing, both at the signal level to discover acoustic classes and using multi-modal sensory input to associate words to meanings. The two aspects, however, were considered independently.

The project will contribute to the development of more flexible speech interfaces in the attempt to increase their use by the general public. The project will also contribute to basic research in cognitive science on modelling language acquisition in humans.



Period: 2010-01-01 - 2013-12-31

Keywords:
asr, machine learning, speech

Project URL:
http://www.speech.kth.se/~giampi

SOURCE OF FUNDING (1/1) 

VR (The Swedish Research Council)


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