In this paper, Out-Of-Language (OOL) detection problem is handled by both language dependent (LD) and language independent (LI) approaches. In the LD approach, a novel speech content and language joint recognition algorithm is proposed, which integrates a phone lattice-based vector space modeling language recognition (LRE) backend into the conventional speech decoding procedure. In the LI approach, lattice derived confidence measures are used. Since these two approaches reflect two different dimensions of uncertainties encoded in lattices, combining them improves both the LRE and OOL detection performance. Experiments also show that for LD approach the detection accuracies can be significantly increased by applying heuristic phone lattice reconstruction. Evaluated on a Mandarin/English mixed conversational telephone speech corpus with a Mandarin speech recognizer, the proposed method achieves an EER of 12.68% in OOL detection, and reduces the recognition error by 33.06%.
Bibliographic reference. Shan, Yuxiang / Deng, Yan / Liu, Jia (2011): "Combining lattice-based language dependent and independent approaches for out-of-language detection in LVCSR", In INTERSPEECH-2011, 1933-1936.