  Google's multilingual speech recognition system combines low-level acoustic
signals with language-specific recognizer signals to better predict the
language of an utterance. This paper presents our experience with different
signal combination methods to improve overall language identification accuracy.
We compare the performance of a lattice-based ensemble model and a deep neural
network model to combine signals from recognizers with that of a baseline that
only uses low-level acoustic signals. Experimental results show that the deep
neural network model outperforms the lattice-based ensemble model, and it
reduced the error rate from 5.5% in the baseline to 4.3%, which is a 21.8%
relative reduction.
