Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework
ICLR 2024(2023)
摘要
Connectionist Temporal Classification (CTC) is a widely used criterion for
training supervised sequence-to-sequence (seq2seq) models. It enables learning
the relations between input and output sequences, termed alignments, by
marginalizing over perfect alignments (that yield the ground truth), at the
expense of imperfect alignments. This binary differentiation of perfect and
imperfect alignments falls short of capturing other essential alignment
properties that hold significance in other real-world applications. Here we
propose Align With Purpose, a general Plug-and-Play
framework for enhancing a desired property in models trained with the CTC
criterion. We do that by complementing the CTC with an additional loss term
that prioritizes alignments according to a desired property. Our method does
not require any intervention in the CTC loss function, enables easy
optimization of a variety of properties, and allows differentiation between
both perfect and imperfect alignments. We apply our framework in the domain of
Automatic Speech Recognition (ASR) and show its generality in terms of property
selection, architectural choice, and scale of training dataset (up to 280,000
hours). To demonstrate the effectiveness of our framework, we apply it to two
unrelated properties: emission time and word error rate (WER). For the former,
we report an improvement of up to 570ms in latency optimization with a minor
reduction in WER, and for the latter, we report a relative improvement of 4.5
WER over the baseline models. To the best of our knowledge, these applications
have never been demonstrated to work on a scale of data as large as ours.
Notably, our method can be implemented using only a few lines of code, and can
be extended to other alignment-free loss functions and to domains other than
ASR.
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关键词
ASR,CTC,seq2seq,low latency,WER
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