  The acoustic and linguistic features are important cues for the spoken
language identification (LID) task. Recent advanced LID systems mainly use
acoustic features that lack the usage of explicit linguistic feature encoding.
In this paper, we propose a novel transducer-based language embedding approach
for LID tasks by integrating an RNN transducer model into a language embedding
framework. Benefiting from the advantages of the RNN transducer's linguistic
representation capability, the proposed method can exploit both
phonetically-aware acoustic features and explicit linguistic features for LID
tasks. Experiments were carried out on the large-scale multilingual LibriSpeech
and VoxLingua107 datasets. Experimental results showed the proposed method
significantly improves the performance on LID tasks with 12% to 59% and 16% to
24% relative improvement on in-domain and cross-domain datasets, respectively.
