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arXiv · 2110.03098

CTC Variations Through New WFST Topologies

Aleksandr Laptev·Somshubra Majumdar·Boris Ginsburg
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Abstract

This paper presents novel Weighted Finite-State Transducer (WFST) topologies to implement Connectionist Temporal Classification (CTC)-like algorithms for automatic speech recognition. Three new CTC variants are proposed: (1) the "compact-CTC", in which direct transitions between units are replaced with back-off transitions; (2) the "minimal-CTC", that only adds self-loops when used in WFST-composition; and (3) the "selfless-CTC" variants, which disallows self-loop for non-blank units. Compact-CTC allows for 1.5 times smaller WFST decoding graphs and reduces memory consumption by two times when training CTC models with the LF-MMI objective without hurting the recognition accuracy. Minimal-CTC reduces graph size and memory consumption by two and four times for the cost of a small accuracy drop. Using selfless-CTC can improve the accuracy for wide context window models.

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BibTeXRIS

Aleksandr Laptev, Somshubra Majumdar, Boris Ginsburg. 2021-10-06. CTC Variations Through New WFST Topologies. https://doi.org/10.21437/interspeech.2022-10854

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