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Preference-Based Learning in Audio Applications: A Systematic Analysis

Aaron Broukhim, Yiran Shen, Prithviraj Ammanabrolu, Nadir Weibel

TL;DR

This systematic analysis evaluates how preference-based learning has been applied to audio, uncovering a nascent but rapidly evolving field. It traces a shift from pre-modern learning-to-rank approaches in emotion recognition to modern RLHF/DPO-driven generation tasks, and identifies multi-dimensional evaluation, metric misalignment, and multi-stage training as core patterns. The study highlights the need for standardized benchmarks, richer datasets, and careful consideration of audio-specific temporal factors to reliably capture subjective listener judgments. The findings underscore the potential of preference learning to improve naturalness, expressivity, and musicality in speech and music applications, with broad implications for future cross-modal and text-to-audio systems.

Abstract

Despite the parallel challenges that audio and text domains face in evaluating generative model outputs, preference learning remains remarkably underexplored in audio applications. Through a PRISMA-guided systematic review of approximately 500 papers, we find that only 30 (6%) apply preference learning to audio tasks. Our analysis reveals a field in transition: pre-2021 works focused on emotion recognition using traditional ranking methods (rankSVM), while post-2021 studies have pivoted toward generation tasks employing modern RLHF frameworks. We identify three critical patterns: (1) the emergence of multi-dimensional evaluation strategies combining synthetic, automated, and human preferences; (2) inconsistent alignment between traditional metrics (WER, PESQ) and human judgments across different contexts; and (3) convergence on multi-stage training pipelines that combine reward signals. Our findings suggest that while preference learning shows promise for audio, particularly in capturing subjective qualities like naturalness and musicality, the field requires standardized benchmarks, higher-quality datasets, and systematic investigation of how temporal factors unique to audio impact preference learning frameworks.

Preference-Based Learning in Audio Applications: A Systematic Analysis

TL;DR

This systematic analysis evaluates how preference-based learning has been applied to audio, uncovering a nascent but rapidly evolving field. It traces a shift from pre-modern learning-to-rank approaches in emotion recognition to modern RLHF/DPO-driven generation tasks, and identifies multi-dimensional evaluation, metric misalignment, and multi-stage training as core patterns. The study highlights the need for standardized benchmarks, richer datasets, and careful consideration of audio-specific temporal factors to reliably capture subjective listener judgments. The findings underscore the potential of preference learning to improve naturalness, expressivity, and musicality in speech and music applications, with broad implications for future cross-modal and text-to-audio systems.

Abstract

Despite the parallel challenges that audio and text domains face in evaluating generative model outputs, preference learning remains remarkably underexplored in audio applications. Through a PRISMA-guided systematic review of approximately 500 papers, we find that only 30 (6%) apply preference learning to audio tasks. Our analysis reveals a field in transition: pre-2021 works focused on emotion recognition using traditional ranking methods (rankSVM), while post-2021 studies have pivoted toward generation tasks employing modern RLHF frameworks. We identify three critical patterns: (1) the emergence of multi-dimensional evaluation strategies combining synthetic, automated, and human preferences; (2) inconsistent alignment between traditional metrics (WER, PESQ) and human judgments across different contexts; and (3) convergence on multi-stage training pipelines that combine reward signals. Our findings suggest that while preference learning shows promise for audio, particularly in capturing subjective qualities like naturalness and musicality, the field requires standardized benchmarks, higher-quality datasets, and systematic investigation of how temporal factors unique to audio impact preference learning frameworks.

Paper Structure

This paper contains 45 sections, 1 equation, 2 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Preference Learning Fundamentals
  4. Why Audio is Suited for Preference Learning
  5. Methods
  6. Keyword Selection
  7. Published Works
  8. Non-Published Works
  9. Cited Works
  10. Final Selection and Inclusion
  11. Results
  12. Tasks
  13. Pre-Modern Preference Learning (Learning to Rank)
  14. Modern Preference Learning
  15. Preference Reward Models
  16. ...and 30 more sections

Figures (2)

  • Figure 1: A visual representation of RLHF and DPO training loops. Both pipelines implement RLHF; the key difference is whether the reward model is explicit (left) or implicit (right). MLE stand for Maximum Likelihood Estimation.
  • Figure 2: PRISMA-inspired diagram illustrating the literature screening process. Papers were excluded for one of three reasons: (1) absence of a preference learning framework, (2) lack of focus on the audio domain, or (3) being a survey.