The Artificial Larynx Transducer (ALT), as a possibility to re-obtain audible speech for
people who had to undergo a total laryngectomy, is known
since 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 the daily live, it is necessary to use the known technology
to improve the quality of live 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 (F0) - contour. In this paper we present a new method to automatically learn an artificial F0-contour. The used model 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.
Index Terms: alaryngeal speech, Artificial Larynx Transducer (ALT), fundamental frequency, speech enhancement, laryngectomy, GMM
Bibliographic reference. Fuchs, Anna Katharina / Hagmüller, Martin (2012): "Learning an artificial F0-contour for ALT speech", In INTERSPEECH-2012, 70-73.