BAYESIAN FRAMEWORK FOR VOICING ALTERNATION & ASSIMILATION STUDIES ON LARGE CORPORA IN FRENCH
Martine Adda‐Decker, Pierre Hallé · 2007
The presented work aims at exploring voicing alternation and assimilation on very large corpora using a Bayesian framework. A voice 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 voice 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 voice features. Using a corpus of 90h of French journalistic speech, an overall voicing alternation rate of 2.7% has been measured, thus calibrating the method’s accuracy. The 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.