1. A computer implemented method comprising: obtaining a representation of an input acoustic sequence, the input acoustic sequence representing an utterance; processing the representation of the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) to generate, for each position in an output sequence order, a set of substring scores that includes a respective substring score for each substring in a set of substrings, comprising, for each position after an initial position in the output sequence order: processing a substring at the preceding position in the output sequence order and the attention context vector for the preceding position in the order using the attention-based RNN to update the hidden state of the attention-based RNN from the hidden state for the preceding position to a hidden state for the position; generating an attention context vector for the position from the representation and the RNN hidden state for the position in the output sequence order; and generating the set of substring scores for the position using the attention context vector for the position and the RNN hidden state for the position; and generating a sequence of substrings that represent a transcription of the utterance.
1. A computer implemented method comprising: obtaining a representation of an input acoustic sequence, the input acoustic sequence representing an utterance; processing the representation of the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) to generate, for each position in an output sequence order, a set of substring scores that includes a respective substring score for each substring in a set of substrings, comprising, for each position after an initial position in the output sequence order: processing a substring at the preceding position in the output sequence order and the attention context vector for the preceding position in the order using the attention-based RNN to update the hidden state of the attention-based RNN from the hidden state for the preceding position to a hidden state for the position; generating an attention context vector for the position from the representation and the RNN hidden state for the position in the output sequence order; and generating the set of substring scores for the position using the attention context vector for the position and the RNN hidden state for the position; and generating a sequence of substrings that represent a transcription of the utterance. 13. The method of claim 1 , wherein the generated sequence of substrings begins with a start of sequence token <sos> and ends with an end of sequence token <eos>.