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ARECHO: Autoregressive Evaluation via Chain-Based Hypothesis Optimization for Speech Multi-Metric Estimation

Jiatong Shi, Yifan Cheng, Bo-Hao Su, Hye-jin Shim, Jinchuan Tian, Samuele Cornell, Yiwen Zhao, Siddhant Arora, Shinji Watanabe

TL;DR

ARECHO addresses the challenge of jointly estimating multiple speech quality and related metrics that vary in scale and type, using an autoregressive, chain-based approach. The method unifies heterogeneous metrics through a tokenization framework, learns inter-metric dependencies with a dynamic classifier chain, and improves inference reliability via a two-step confidence-oriented decoding scheme. Empirical results across corrupted, enhanced, and synthesized speech show ARECHO consistently outperforms strong baselines, enabling subset queries, partial supervision, and reference-free evaluation with improved interpretability. The framework offers a scalable, flexible tool for holistic speech evaluation with practical benefits for benchmarking and deployment in diverse real-world settings.

Abstract

Speech signal analysis poses significant challenges, particularly in tasks such as speech quality evaluation and profiling, where the goal is to predict multiple perceptual and objective metrics. For instance, metrics like PESQ (Perceptual Evaluation of Speech Quality), STOI (Short-Time Objective Intelligibility), and MOS (Mean Opinion Score) each capture different aspects of speech quality. However, these metrics often have different scales, assumptions, and dependencies, making joint estimation non-trivial. To address these issues, we introduce ARECHO (Autoregressive Evaluation via Chain-based Hypothesis Optimization), a chain-based, versatile evaluation system for speech assessment grounded in autoregressive dependency modeling. ARECHO is distinguished by three key innovations: (1) a comprehensive speech information tokenization pipeline; (2) a dynamic classifier chain that explicitly captures inter-metric dependencies; and (3) a two-step confidence-oriented decoding algorithm that enhances inference reliability. Experiments demonstrate that ARECHO significantly outperforms the baseline framework across diverse evaluation scenarios, including enhanced speech analysis, speech generation evaluation, and, noisy speech evaluation. Furthermore, its dynamic dependency modeling improves interpretability by capturing inter-metric relationships. Across tasks, ARECHO offers reference-free evaluation using its dynamic classifier chain to support subset queries (single or multiple metrics) and reduces error propagation via confidence-oriented decoding.

ARECHO: Autoregressive Evaluation via Chain-Based Hypothesis Optimization for Speech Multi-Metric Estimation

TL;DR

ARECHO addresses the challenge of jointly estimating multiple speech quality and related metrics that vary in scale and type, using an autoregressive, chain-based approach. The method unifies heterogeneous metrics through a tokenization framework, learns inter-metric dependencies with a dynamic classifier chain, and improves inference reliability via a two-step confidence-oriented decoding scheme. Empirical results across corrupted, enhanced, and synthesized speech show ARECHO consistently outperforms strong baselines, enabling subset queries, partial supervision, and reference-free evaluation with improved interpretability. The framework offers a scalable, flexible tool for holistic speech evaluation with practical benefits for benchmarking and deployment in diverse real-world settings.

Abstract

Speech signal analysis poses significant challenges, particularly in tasks such as speech quality evaluation and profiling, where the goal is to predict multiple perceptual and objective metrics. For instance, metrics like PESQ (Perceptual Evaluation of Speech Quality), STOI (Short-Time Objective Intelligibility), and MOS (Mean Opinion Score) each capture different aspects of speech quality. However, these metrics often have different scales, assumptions, and dependencies, making joint estimation non-trivial. To address these issues, we introduce ARECHO (Autoregressive Evaluation via Chain-based Hypothesis Optimization), a chain-based, versatile evaluation system for speech assessment grounded in autoregressive dependency modeling. ARECHO is distinguished by three key innovations: (1) a comprehensive speech information tokenization pipeline; (2) a dynamic classifier chain that explicitly captures inter-metric dependencies; and (3) a two-step confidence-oriented decoding algorithm that enhances inference reliability. Experiments demonstrate that ARECHO significantly outperforms the baseline framework across diverse evaluation scenarios, including enhanced speech analysis, speech generation evaluation, and, noisy speech evaluation. Furthermore, its dynamic dependency modeling improves interpretability by capturing inter-metric relationships. Across tasks, ARECHO offers reference-free evaluation using its dynamic classifier chain to support subset queries (single or multiple metrics) and reduces error propagation via confidence-oriented decoding.

Paper Structure

This paper contains 50 sections, 17 equations, 5 figures, 22 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. ARECHO
  4. Base Task Formulation
  5. Tokenizing Everything
  6. Dynamic Classifier Chain
  7. Two-step Confidence-oriented Decoding
  8. Experimental Setup
  9. Datasets
  10. Model Setups
  11. Evaluation
  12. Experimental Results
  13. Conclusion
  14. Terminology: Definition and use of "Metric"
  15. Tokenization with Probability Density Functions
  16. ...and 35 more sections

Figures (5)

  • Figure 1: Visualization of metric order for enhanced speech test set via color blocks (Red = Early, Blue = Late).
  • Figure 2: Visualization of metric order for corrupted speech test set via color blocks (Red = Early, Blue = Late).
  • Figure 3: Visualization of metric order for synthesized speech test set via color blocks (Red = Early, Blue = Late).
  • Figure 4: Average training time per epoch for UniVERSA, UniVERSA-T, and ARECHO on the Base training set.. The proposed ARECHO model demonstrates improved training efficiency.
  • Figure 5: The ARECHO logo: a visual representation of chain-based, autoregressive speech evaluation.