Prosodic attribute model for spoken language identification

Prosodic information is believed to carry language-specific information useful to spoken language recognition. Modeling prosodic features is a challenging problem, on which a wide diversity of approaches have been investigated. In this paper, a novel prosodic attribute model (PAM) is proposed to capture prosodic features with compact models. It models the language-specific co-occurrence statistics of a comprehensive set of prosodic features. When the prosodic LID system with PAM is evaluated in NIST Language Recognition Evaluations (LRE) 2007 and 2009, it demonstrates respectively 21% and 11% relative EER reduction compared to a phonotactic LID system. The contributions of prosodic features in detecting some of the target languages, including tonal languages, are even more substantial. It is also noted that most prosodic attributes in the comprehensive set are making positive contributions.