Learning An Artificial F-0-Contour For Alt Speech

The Artificial Larynx Transducer (ALT) as a possibility to re-obtain audible speech for people who had to undergo a total laryngectomy has been known for decades. Not only the design and underlying technique but also the poor speech quality and intelligibility have not improved until now. In a world where technology rules everyday life, it is necessary to use the known technology to improve the quality of life for handicapped people.One reason for the lack of naturalness is the constant vibration of the ALT. A method to substantially improve ALT speech is to introduce a varying fundamental frequency (F-0) - contour. In this paper we present a new method to automatically learn an artificial F-0-contour. The model used is a Gaussian mixture model (GMM) which is trained with a database containing speech of ALT users as well as healthy people. Informal listening tests suggest that this approach is a first step for a subsequent overall enhancement technique for speech produced by an ALT.