This paper presents a novel algorithm for building an automatic speech
recognition (ASR) model with imperfect training data. Imperfectly transcribed
speech is a prevalent issue in human-annotated speech corpora, which degrades
the performance of ASR models. To address this problem, we propose Bypass
Temporal Classification (BTC) as an expansion of the Connectionist Temporal
Classification (CTC) criterion. BTC explicitly encodes the uncertainties
associated with transcripts during training. This is accomplished by enhancing
the flexibility of the training graph, which is implemented as a weighted
finite-state transducer (WFST) composition. The proposed algorithm improves the
robustness and accuracy of ASR systems, particularly when working with
imprecisely transcribed speech corpora. Our implementation will be
open-sourced.

提出了一种利用 Bypass Temporal Classification (BTC) 方法来扩大微调自动语音识别模型 (CNN) 准确性的算法，并且该算法构建了一个基于加权有限状态转换器 (WFST) 的可变性训练图来明确地编码训练期间的不确定性，从而改善了处理精度不高的语音资源库时 ASR 系统的健壮性和准确性。