On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation

TL;DR

This paper critiques the use of global token perplexity for evaluating spoken language models, proposing alternative metrics that better align with human judgments and reshape model performance comparisons.

Contribution

It introduces new likelihood- and generative-based evaluation methods tailored for speech, addressing limitations of traditional text-based perplexity metrics.

Findings

New metrics correlate better with human opinion scores

Revised evaluation reduces performance gap between models and humans

Traditional perplexity underestimates speech model capabilities

Abstract

Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content while preserving attributes like speaker and emotion, serving as foundation models for spoken dialogue. In prior literature, these models are often evaluated using ``global token perplexity'', which directly applies the text perplexity formulation to speech tokens. However, this practice overlooks fundamental differences between speech and text modalities, possibly leading to an underestimation of the speech characteristics. In this work, we propose a variety of likelihood- and generative-based evaluation methods that serve in place of naive global token perplexity. We demonstrate that the proposed evaluations more faithfully reflect perceived generation quality, as evidenced by stronger correlations with human-rated mean opinion scores (MOS). When assessed under the new metrics, the relative performance landscape of spoken language models is reshaped, revealing a significantly reduced gap between the best-performing model and the human topline. Together, these results suggest that appropriate evaluation is critical for accurately assessing progress in spoken language modeling.