Rhythmic Proximity Between Natives And Learners Of French - Evaluation of a metric based on the CEFC corpus

This work aims to better understand the role of rhythm in foreign accent, and its modelling. We made a model of rhythm in French taking into account its variability, thanks to the Corpus pour l{'}{\'E}tude du Fran{\c{c}}ais Contemporain (CEFC), which contains up to 300 hours of speech of a wide variety of speaker profiles and situations. 16 parameters were computed, each of them being based on segment duration, such as voicing and intersyllabic timing. All the parameters are fully automatically detected from signal, without ASR or transcription. A gaussian mixture model was trained on 1,340 native speakers of French; any 30-second minimum speech may be computed to get the probability of its belonging to this model. We tested it with 146 test native speakers (NS), 37 non-native speakers (NNS) from the same corpus, and 29 non-native Japanese learners of French (JpNNS) from an independent corpus. The probability of NNS having inferior log-likelihood to NS was only a tendency (p=.067), maybe due to the heterogeneity of French proficiency of the speakers; but a much bigger probability was obtained for JpNNS (p{\textless}.0001), where all speakers were A2 level. Eta-squared test showed that most efficient parameters were intersyllabic mean duration and variation coefficient, along with speech rate for NNS; and speech rate and phonation ratio for JpNNS.