  The goal of this contribution is to use a parametric speech synthesis system
for reducing background noise and other interferences from recorded speech
signals. In a first step, Hidden Markov Models of the synthesis system are
trained.
  Two adequate training corpora consisting of text and corresponding speech
files have been set up and cleared of various faults, including inaudible
utterances or incorrect assignments between audio and text data. Those are
tested and compared against each other regarding e.g. flaws in the synthesized
speech, it's naturalness and intelligibility. Thus different voices have been
synthesized, whose quality depends less on the number of training samples used,
but much more on the cleanliness and signal-to-noise ratio of those.
Generalized voice models have been used for synthesis and the results greatly
differ between the two speech corpora.
  Tests regarding the adaptation to different speakers show that a resemblance
to the original speaker is audible throughout all recordings, yet the
synthesized voices sound robotic and unnatural in smaller parts. The spoken
text, however, is usually intelligible, which shows that the models are working
well.
  In a novel approach, speech is synthesized using side information of the
original audio signal, particularly the pitch frequency. Results show an
increase of speech quality and intelligibility in comparison to speech
synthesized solely from text, up to the point of being nearly indistinguishable
from the original.
