Revisiting the Relation Between Language Model Perplexity and ASR Word Error Rate for Modern End-to-End Speech Recognition

2026-07-06

AI Summary

This study investigates whether the well-established log-linear relationship between language model perplexity (PPL) and word error rate (WER) in automatic speech recognition (ASR) still holds for modern end-to-end ASR systems. Given that contemporary architectures commonly incorporate internal language modeling (ILM) capabilities, we systematically evaluate the efficacy of external language models, examining the PPL–WER correlation, the impact of encoder context length, and the applicability of large language models. For the first time, we comprehensively analyze how ILM—and its removal—affects the PPL–WER relationship. Our experiments reveal that while external language models continue to yield performance gains, the presence of ILM substantially shifts the PPL–WER trend; this shift is markedly altered upon ILM subtraction, indicating that neglecting internal language modeling leads to misleading assessments of external language model effectiveness.

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.

Problem

• language model perplexity
• word error rate
• end-to-end ASR
• internal language modeling
• large language models

Innovation

• perplexity
• word error rate
• internal language modeling
• end-to-end ASR
• large language models