Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework

• URL: http://arxiv.org/abs/2307.01715v3
• Date: Thu, 7 Mar 2024 17:59:25 GMT
• Title: Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework
• Authors: Eliya Segev, Maya Alroy, Ronen Katsir, Noam Wies, Ayana Shenhav, Yael Ben-Oren, David Zar, Oren Tadmor, Jacob Bitterman, Amnon Shashua and Tal Rosenwein
• Abstract summary: Connectionist Temporal Classification ( CTC) is a widely used criterion for training sequence-to-sequence (seq2seq) models. We propose $textitAlign With Purpose, a $textbfgeneral Plug-and-Play framework for enhancing a desired property in models trained with the CTC criterion. 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.
• Score: 8.228892600588765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\textit{Align With Purpose}$, a $\textbf{general Plug-and-Play framework}$ 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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