IKFST: IOO and KOO Algorithms for Accelerated and Precise WFST-based End-to-End Automatic Speech Recognition

TL;DR

This paper introduces IKFST algorithms that leverage the structural roles of blank and non-blank frames in CTC outputs to significantly accelerate WFST-based end-to-end speech recognition without sacrificing accuracy.

Contribution

The paper proposes novel decoding algorithms, Keep-Only-One and Insert-Only-One, that improve inference speed by exploiting CTC frame structures in WFST-based speech recognition.

Findings

Achieved state-of-the-art accuracy on multiple datasets.

Reduced decoding latency substantially.

Demonstrated compatibility with large-scale speech systems.

Abstract

End-to-end automatic speech recognition has become the dominant paradigm in both academia and industry. To enhance recognition performance, the Weighted Finite-State Transducer (WFST) is widely adopted to integrate acoustic and language models through static graph composition, providing robust decoding and effective error correction. However, WFST decoding relies on a frame-by-frame autoregressive search over CTC posterior probabilities, which severely limits inference efficiency. Motivated by establishing a more principled compatibility between WFST decoding and CTC modeling, we systematically study the two fundamental components of CTC outputs, namely blank and non-blank frames, and identify a key insight: blank frames primarily encode positional information, while non-blank frames carry semantic content. Building on this observation, we introduce Keep-Only-One and Insert-Only-One, two decoding algorithms that explicitly exploit the structural roles of blank and non-blank frames to achieve significantly faster WFST-based inference without compromising recognition accuracy. Experiments on large-scale in-house, AISHELL-1, and LibriSpeech datasets demonstrate state-of-the-art recognition accuracy with substantially reduced decoding latency, enabling truly efficient and high-performance WFST decoding in modern speech recognition systems.