# Promising Accurate Prefix Boosting for sequence-to-sequence ASR

arXiv: https://arxiv.org/abs/1811.02770 · HTML: https://mlsanity.com/paper/1811.02770/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Sentence-Level or Token-Level? A Comprehensive Study on Knowledge Distillation](https://mlsanity.com/paper/2404.14827/) (2024)
- [On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models](https://mlsanity.com/paper/2107.04154/) (2021)
- [O-1: Self-training with Oracle and 1-best Hypothesis](https://mlsanity.com/paper/2308.07486/) (2023)

## Abstract (arXiv)

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.