A linguistic data acquisition front-end for language recognition evaluation

One of the major challenges of the language identification (LID) system comes from the sparse training data. Manually collecting the linguistic data through the controlled studio is usually expensive and impractical. But multilingual broadcast programs (Voice of America, for instance) can be collected as a reasonable alternative to the linguistic data acquisition issue. However, unlike studio collected linguistic data, broadcast programs usually contain many contents other than pure linguistic data: musical contents in foreground/background, commercials, noise from practical life. In this study, a systematic processing approach is proposed to extract the linguistic data from the broadcast media. The experimental results obtained on NIST LRE 2009 data show that the proposed method can provide 22.2% relative improvement of segmentation accuracy and 20.5% relative improvement of LID accuracy.