The ART of Conversation: Measuring Phonetic Convergence and Deliberate
  Imitation in L2-Speech with a Siamese RNN
Zheng Yuan Aldo Pastore Dorina de Jong Hao Xu Luciano Fadiga Alessandro D'Ausilio
Phonetic convergence describes the automatic and unconscious speech
adaptation of two interlocutors in a conversation. This paper proposes a
Siamese recurrent neural network (RNN) architecture to measure the convergence
of the holistic spectral characteristics of speech sounds in an L2-L2
interaction. We extend an alternating reading task (the ART) dataset by adding
20 native Slovak L2 English speakers. We train and test the Siamese RNN model
to measure phonetic convergence of L2 English speech from three different
native language groups: Italian (9 dyads), French (10 dyads) and Slovak (10
dyads). Our results indicate that the Siamese RNN model effectively captures
the dynamics of phonetic convergence and the speaker's imitation ability.
Moreover, this text-independent model is scalable and capable of handling
L1-induced speaker variability.