A Generalized Formalism of Auto-Regressive Decoding for Speech Processing

2026-06-16

AI Summary

This work addresses the absence of a unified theoretical framework for autoregressive decoding strategies in speech processing, which has led to ambiguous definitions, inconsistent taxonomies, and difficulties in fair comparison. The paper introduces, for the first time, a general formal framework that precisely specifies inclusion criteria for autoregressive search and systematically categorizes and describes decoding strategies employed in neural speech generation models. By clarifying conceptual boundaries, the framework enhances comparability and evaluation consistency across strategies, streamlines the design of decoding-centric benchmarking protocols, and enables ablation studies focused specifically on search mechanisms. Consequently, it facilitates standardized analysis of inference-stage behavior in speech generation models.

Abstract

In speech processing, most state-of-the-art sequence prediction models rely on auto-regressive (AR) strategies to generate output sequences based on the raw predictions of the model. Despite their crucial role in the inference process, a comprehensive overview of AR strategies as a unified field is lacking, due largely to implicit and multiple definitions of next-token decoding. This context complicates the choice, comparison, and evaluation of strategies, while creating inconsistencies in the characterization of approaches as auto-regressive or not. We begin by setting explicit inclusion criteria for the field of AR search in speech processing, and derive a generalized theoretical framework to categorize and report on search strategies for neural models. We show the capabilities of this formalism in simplifying the design of benchmarks centered around the decoding process, allowing for ablation studies that are focused on search strategies.

Problem

• auto-regressive decoding
• speech processing
• sequence prediction
• search strategies
• decoding framework

Innovation

• auto-regressive decoding
• speech processing
• generalized formalism
• search strategies
• neural sequence modeling