The presented work aims at exploring voicing alternation and
assimilation on very large corpora using a Bayesian framework.  A
voicing feature (VF) variable has been introduced whose value is
determined using statistical acoustic phoneme models, corresponding to
3-state gaussian mixture Hidden Markov Models. For all relevant
consonants, i.e. oral plosives and fricatives, their surface form
voicing feature is determined by maximising the acoustic likelihood of
the competing phoneme models.  A voicing alternation (VA) measure
counts the number of changes between underlying and surface form
voicing features.  Using a corpus of 70h of French journalistic
speech, an overall voicing alternation rate of 2.7% has been
measured, thus calibrating the method's accuracy. VA rate remains
below 2% word-internally and on word starts and raises up to 9% on
lexical word endings. In assimilation contexts rates grow
significantly (>20%) highlighting regressive voicing assimilation.
Results also exhibit a weak tendency for progressive devoicing.