  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.

Computation and Language;Machine Learning;Sound;Audio and Speech Processing