CTC Variations Through New WFST Topologies

• URL: http://arxiv.org/abs/2110.03098v1
• Date: Wed, 6 Oct 2021 23:00:15 GMT
• Title: CTC Variations Through New WFST Topologies
• Authors: Aleksandr Laptev, Somshubra Majumdar, Boris Ginsburg
• Abstract summary: 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 epsilon> back-off transitions; (2) the "minimal-CTC", that only adds blank> self-loops when used in WFST-composition; and (3) "selfless-CTC", that disallows self-loop for non-blank units.
• Score: 79.94035631317395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 <epsilon> back-off transitions; (2) the "minimal-CTC", that only adds <blank> self-loops when used in WFST-composition; and (3) "selfless-CTC", that disallows self-loop for non-blank units. The new CTC variants have several benefits, such as reducing decoding graph size and GPU memory required for training while keeping model accuracy.

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