This paper describes the language identification (LID) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We show that techniques originally developed for LID on telephone speech (e.g., for the NIST language recognition evaluations) remain effective on the noisy RATS data,provided that careful consideration is applied when designing the training and development sets. In addition, we show significant improvements from the use of Wiener filtering, neural network based i-vector, language dependent i-vector modeling, and fusion.
Index Terms: language identification, noisy speech
Bibliographic reference. Matějka, Pavel / Plchot, Oldřich / Soufifar, Mehdi / Glembek, Ondřej / D'Haro, Luis Fernando / Veselý, Karel / Grézl, František / Ma, Jeff / Matsoukas, Spyros / Dehak, Najim (2012): "Patrol team language identification system for DARPA RATS P1 evaluation", In INTERSPEECH-2012, 50-53.