10. The method of claim 1 , wherein processing the alternative representation for the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) comprises, 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 an 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 alternative 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.
10. The method of claim 1 , wherein processing the alternative representation for the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) comprises, 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 an 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 alternative 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. 12. The method of claim 10 , wherein generating an attention context vector for the position from the alternative representation and the RNN hidden state for the position in the output sequence order comprises: computing a scalar energy for the position using the alternative representation and the hidden state of the attention-based RNN for the position; converting the computed scalar energy into a probability distribution using a softmax function; and using the probability distribution to create a context vector by combining the alternative representation at different positions.