  The transcriptions used to train an Automatic Speech Recognition (ASR) system
may contain errors. Usually, either a quality control stage discards
transcriptions with too many errors, or the noisy transcriptions are used as
is. We introduce Lead2Gold, a method to train an ASR system that exploits the
full potential of noisy transcriptions. Based on a noise model of transcription
errors, Lead2Gold searches for better transcriptions of the training data with
a beam search that takes this noise model into account. The beam search is
differentiable and does not require a forced alignment step, thus the whole
system is trained end-to-end. Lead2Gold can be viewed as a new loss function
that can be used on top of any sequence-to-sequence deep neural network. We
conduct proof-of-concept experiments on noisy transcriptions generated from
letter corruptions with different noise levels. We show that Lead2Gold obtains
a better ASR accuracy than a competitive baseline which does not account for
the (artificially-introduced) transcription noise.
