Revisiting the Relation Between Language Model Perplexity and ASR Word Error Rate for Modern End-to-End Speech Recognition
arXiv:2607.05612v1 Announce Type: new 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 an
The proliferation of advanced end-to-end ASR systems and the integration of neural LMs/LLMs necessitate a re-evaluation of traditional metrics for speech recognition performance.
Understanding the true relationship between language model performance and ASR accuracy is crucial for optimizing speech-to-text technologies, which underpin numerous AI applications.
The established linear relationship between perplexity and word error rate is being challenged, suggesting a more nuanced evaluation approach is needed for modern speech AI.
- · AI research labs developing novel ASR architectures
- · Companies investing in advanced speech AI
- · Developers leveraging LLMs for improved speech interfaces
- · ASR systems relying solely on outdated PPL metrics
- · Companies with less sophisticated speech recognition R&D
- · Benchmarks that don't account for modern ASR complexities
More accurate and efficient development of next-generation speech recognition models, potentially leading to widespread adoption in various products.
Improved human-computer interaction through more reliable and context-aware voice interfaces, accelerating the integration of AI into daily life.
The development of a new standard metric or a more complex evaluation framework for speech AI, influencing future research and investment in the field.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL