  This paper addresses spoken language identification (SLI) and speech
recognition of multilingual broadcast and institutional speech, real
application scenarios that have been rarely addressed in the SLI literature.
Observing that in these domains language changes are mostly associated with
speaker changes, we propose a cascaded system consisting of speaker diarization
and language identification and compare it with more traditional language
identification and language diarization systems. Results show that the proposed
system often achieves lower language classification and language diarization
error rates (up to 10% relative language diarization error reduction and 60%
relative language confusion reduction) and leads to lower WERs on multilingual
test sets (more than 8% relative WER reduction), while at the same time does
not negatively affect speech recognition on monolingual audio (with an absolute
WER increase between 0.1% and 0.7% w.r.t. monolingual ASR).
