Revisiting the Relation Between Language Model Perplexity and ASR Word Error Rate for Modern End-to-End Speech Recognition
This study re-evaluates the traditional linear relationship between language model perplexity and automatic speech recognition word error rate. It finds that modern end-to-end ASR systems, with their built-in language modeling capacity, challenge this assumption. The paper examines whether external LMs still improve current systems, the linearity of the PPL-WER relation in log-log space, the effect of encoder context length, and how LLM perplexities fit with standard neural LMs. Additionally, internal language modeling in attention-based encoder-decoder systems is investigated, showing that ILM subtraction alters the observed relation, highlighting the importance of considering the decoder's internal LM when interpreting external LM quality.
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[Submitted on 6 Jul 2026]
Title:Revisiting the Relation Between Language Model Perplexity and ASR Word Error Rate for Modern End-to-End Speech Recognition
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Abstract:Language model (LM) perplexity (PPL) has historically been used as a proxy for automatic speech recognition (ASR) word error rate (WER), with prior work reporting an approximately linear relation in log-log space. Modern end-to-end ASR systems challenge this assumption because they already contain internal language modeling capacity, are often evaluated without external language models, and can now be combined with neural LMs and large language models (LLMs) through different recognition strategies. This paper revisits the relation between PPL and WER for modern ASR systems. We study whether external LMs still improve current end-to-end ASR systems, whether the PPL-WER relation remains linear in log-log space, how encoder context length affects this relation, and how LLM perplexities fit into the trend observed for standard neural LMs. We further investigate internal language modeling (ILM) in attention-based encoder-decoder systems and show that ILM subtraction changes the observed PPL-WER relation, indicating that the decoder's internal LM must be considered when interpreting the effect of external LM quality.
Comments: Submitted to SLT 2026
Subjects:
Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2607.05612 [cs.CL]
(or arXiv:2607.05612v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.05612
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohammad Zeineldeen [view email] [v1] Mon, 6 Jul 2026 20:15:49 UTC (197 KB)
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