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Multilingual ASR technology simplifies model training and deployment, but itsaccuracy is known to depend on the availability of language information atruntime. Since language identity is seldom known beforehand in real-worldscenarios, it must be inferred on-the-fly with minimum latency. Furthermore, invoice-activated smart assistant systems, language identity is also required fordownstream processing of ASR output. In this paper, we introduce streaming,end-to-end, bilingual systems that perform both ASR and language identification(LID) using the recurrent neural network transducer (RNN-T) architecture. Onthe input side, embeddings from pretrained acoustic-only LID classifiers areused to guide RNN-T training and inference, while on the output side, languagetargets are jointly modeled with ASR targets. The proposed method is applied totwo language pairs: English-Spanish as spoken in the United States, andEnglish-Hindi as spoken in India. Experiments show that for English-Spanish,the bilingual joint ASR-LID architecture matches monolingual ASR andacoustic-only LID accuracies. For the more challenging (owing towithin-utterance code switching) case of English-Hindi, English ASR and LIDmetrics show degradation. Overall, in scenarios where users switch dynamicallybetween languages, the proposed architecture offers a promising simplificationover running multiple monolingual ASR models and an LID classifier in parallel.

Streaming End-to-End Bilingual ASR Systems with Joint Language Identification