Phonotactic Language Recognition Using MLP Features

This paper describes a very efficient Parallel Phone Recognizers followed by Language Modeling (PPRLM) system in terms of both performance and processing speed. The system uses context-independent phone recognizers trained on MLP features concatenated with the conventional PLP and pitch features. MLP features have several interesting properties that make them suitable for speech processing, in particular the temporal context provided to the MLP inputs and the discriminative criterion used to learn the MLP parameters. Results of preliminary experiments conducted on the NIST LRE 2005 for the closed-set task show significant improvements obtained by the proposed system compared with a PPRLM system using context-independent phone models trained on PLP features. Moreover, the proposed system performs as well as a PPRLM system using context-dependent phone models, while running 6 times faster.