A method comprising: receiving, at data processing hardware, audio data for an utterance containing a numeric sequence; decoding, by the data processing hardware, using a recurrent neural network-transducer (RNN-T), end-to-end decoder model, the audio data for the utterance to generate, as output from the recurrent neural network-transducer (RNN-T), end-to-end decoder model, an intermediate transcription of the utterance, wherein the intermediate transcription is a written transcription of the utterance comprising numerical and non-numerical words, the numerical words representing the sequence of numbers; processing, by the data processing hardware, using a neural corrector, the intermediate transcription output from the recurrent neural network-transducer (RNN-T), end-to-end decoder model to generate a final transcription that represents the numeric sequence of the utterance, wherein the final transcription replaces the numerical words in the intermediate transcription with a corresponding numerical representation of the sequence of numbers, wherein processing using a neural corrector comprises: processing only a first portion of the intermediate transcription that includes the numerical words without processing a remaining second portion of the intermediate transcription that includes non-numerical words, wherein the neural corrector was trained on a set of training samples, each training sample comprising a speech recognition hypothesis for a corresponding training utterance and a ground-truth transcription of the corresponding training utterance; and providing, by the data processing hardware, the final transcription representing the numeric sequence of the utterance for output. receiving, at data processing hardware, audio data for an utterance containing a numeric sequence; decoding, by the data processing hardware, using a recurrent neural network-transducer (RNN-T), end-to-end decoder model, the audio data for the utterance to generate, as output from the recurrent neural network-transducer (RNN-T), end-to-end decoder model, an intermediate transcription of the utterance, wherein the intermediate transcription is a written transcription of the utterance comprising numerical and non-numerical words, the numerical words representing the sequence of numbers; processing, by the data processing hardware, using a neural corrector, the intermediate transcription output from the recurrent neural network-transducer (RNN-T), end-to-end decoder model to generate a final transcription that represents the numeric sequence of the utterance, wherein the final transcription replaces the numerical words in the intermediate transcription with a corresponding numerical representation of the sequence of numbers, wherein processing using a neural corrector comprises: processing only a first portion of the intermediate transcription that includes the numerical words without processing a remaining second portion of the intermediate transcription that includes non-numerical words, wherein the neural corrector was trained on a set of training samples, each training sample comprising a speech recognition hypothesis for a corresponding training utterance and a ground-truth transcription of the corresponding training utterance; and providing, by the data processing hardware, the final transcription representing the numeric sequence of the utterance for output.