A computer-implemented method comprising: obtaining a plurality of training data sets each associated with a respective native language that is different than the respective native language of the other training data sets , each training data set comprising a plurality of respective training data samples , each training data sample comprising audio spoken in the respective native language and a corresponding transcription of the audio in a respective native script representing the respective native language; for each respective training data sample of each training data set in the respective native language: transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script; and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample , the respective normalized training data sample comprising the audio spoken in the respective native language and the corresponding transliterated text in the target script; and training, using the normalized training data samples generated from each respective training data sample of each training data set and without providing any language information, a multilingual end-to-end speech recognition model to predict speech recognition results in the target script for corresponding speech utterances spoken in any of the different native languages associated with the plurality of training data sets . obtaining a plurality of training data sets each associated with a respective native language that is different than the respective native language of the other training data sets , each training data set comprising a plurality of respective training data samples , each training data sample comprising audio spoken in the respective native language and a corresponding transcription of the audio in a respective native script representing the respective native language; for each respective training data sample of each training data set in the respective native language: transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script; and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample , the respective normalized training data sample comprising the audio spoken in the respective native language and the corresponding transliterated text in the target script; and transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script; and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample , the respective normalized training data sample comprising the audio spoken in the respective native language and the corresponding transliterated text in the target script; and training, using the normalized training data samples generated from each respective training data sample of each training data set and without providing any language information, a multilingual end-to-end speech recognition model to predict speech recognition results in the target script for corresponding speech utterances spoken in any of the different native languages associated with the plurality of training data sets . <EOS>