Multilingual ASR technology simplifies model training and deployment, but its\naccuracy is known to depend on the availability of language information at\nruntime. Since language identity is seldom known beforehand in real-world\nscenarios, it must be inferred on-the-fly with minimum latency. Furthermore, in\nvoice-activated smart assistant systems, language identity is also required for\ndownstream processing of ASR output. In this paper, we introduce streaming,\nend-to-end, bilingual systems that perform both ASR and language identification\n(LID) using the recurrent neural network transducer (RNN-T) architecture. On\nthe input side, embeddings from pretrained acoustic-only LID classifiers are\nused to guide RNN-T training and inference, while on the output side, language\ntargets are jointly modeled with ASR targets. The proposed method is applied to\ntwo language pairs: English-Spanish as spoken in the United States, and\nEnglish-Hindi as spoken in India. Experiments show that for English-Spanish,\nthe bilingual joint ASR-LID architecture matches monolingual ASR and\nacoustic-only LID accuracies. For the more challenging (owing to\nwithin-utterance code switching) case of English-Hindi, English ASR and LID\nmetrics show degradation. Overall, in scenarios where users switch dynamically\nbetween languages, the proposed architecture offers a promising simplification\nover running multiple monolingual ASR models and an LID classifier in parallel.\n