A Systematic Comparison of Search Space Complexity and Efficiency for Time-Synchronous End-to-End ASR Models

Simon Berger, Larissa Kleppel, Ralf Schlüter · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31259 · open-access PDF

Abstract

Search is a central component of Automatic Speech Recognition (ASR) systems and has been deeply investigated for classical ASR modeling based on Hidden Markov Models (HMMs). However, in work on current sequence-to-sequence ASR architectures it is often simplified to a small fixed-size beam search without external Language Model (LM). In this work, we present a comparative analysis of search space complexity and efficiency for Connectionist Temporal Classification (CTC) and neural transducer models under different pruning strategies and LM configurations. We show that these end-to-end (E2E) mod

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