  In this paper, we present promising accurate prefix boosting (PAPB), a
discriminative training technique for attention based sequence-to-sequence
(seq2seq) ASR. PAPB is devised to unify the training and testing scheme in an
effective manner. The training procedure involves maximizing the score of each
partial correct sequence obtained during beam search compared to other
hypotheses. The training objective also includes minimization of token
(character) error rate. PAPB shows its efficacy by achieving 10.8\% and 3.8\%
WER with and without RNNLM respectively on Wall Street Journal dataset.
