The reviewed record of science sign in
Pith

arxiv: 2210.17017 · v2 · pith:QXRWXOX6 · submitted 2022-10-31 · cs.CL · cs.SD· eess.AS

Blank Collapse: Compressing CTC emission for the faster decoding

Minkyu Jung , Ohhyeok Kwon , Seunghyun Seo , Soonshin Seo This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:QXRWXOX6record.jsonopen to challenge →

classification cs.CL cs.SDeess.AS
keywords decodingbeammethodmodelsearchfasterveryaccuracy
0
0 comments X
read the original abstract

Connectionist Temporal Classification (CTC) model is a very efficient method for modeling sequences, especially for speech data. In order to use CTC model as an Automatic Speech Recognition (ASR) task, the beam search decoding with an external language model like n-gram LM is necessary to obtain reasonable results. In this paper we analyze the blank label in CTC beam search deeply and propose a very simple method to reduce the amount of calculation resulting in faster beam search decoding speed. With this method, we can get up to 78% faster decoding speed than ordinary beam search decoding with a very small loss of accuracy in LibriSpeech datasets. We prove this method is effective not only practically by experiments but also theoretically by mathematical reasoning. We also observe that this reduction is more obvious if the accuracy of the model is higher.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs

    eess.AS 2026-04 unverdicted novelty 6.0

    TASU2 adds controllability over uncertainty and error rate to text-derived CTC simulation, enabling better cross-modal alignment and low-resource adaptation for speech LLMs than prior text-only or TTS methods.