Automatic recognition of regional phonological variation in conversational interaction

One key aspect of face-to-face communication concerns the differences that may exist between speakers’ native regional accents. This paper focuses on the characterization of regional phonological variation in a conversational setting. A new, interactive task was designed in which 12 pairs of participants engaged in a collaborative game leading them to produce a number of purpose-built names. In each game, the participants were native speakers of Southern French and Northern French, respectively. How the names were produced by each of the two participants was automatically determined from the recordings using ASR techniques and a pre-established set of possible regional variants along five phonological dimensions. A naive Bayes classifier was then applied to these phonetic forms, with a view to differentiating the speakers’ native regional accents. The results showed that native regional accent was correctly recognized for 79% of the speakers. These results also revealed or confirmed the existence of accent-dependent differences in how segments are phonetically realized, such as the affrication of /d/ in /di/ sequences. Our data allow us to better characterize the phonological and phonetic patterns associated with regional varieties of French on a large scale and in a natural, interactional situation.