Preference-Based Learning in Audio Applications: A Systematic Analysis

• URL: http://arxiv.org/abs/2511.13936v1
• Date: Mon, 17 Nov 2025 21:42:01 GMT
• Title: Preference-Based Learning in Audio Applications: A Systematic Analysis
• Authors: Aaron Broukhim, Yiran Shen, Prithviraj Ammanabrolu, Nadir Weibel,
• Abstract summary: We show that preference learning remains remarkably underexplored in audio applications.<n>Our findings suggest that while preference learning shows promise for audio, the field requires standardized benchmarks, higher-quality datasets, and systematic investigation of how temporal factors unique to audio impact preference learning frameworks.
• Score: 17.826591259820873
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Optimizing Conversational Quality in Spoken Dialogue Systems with Reinforcement Learning from AI Feedback [82.70507055599093]
  We present the first systematic study of preference learning for improving SDS quality in both multi-turn Chain-of-Thought and blockwise duplex models.<n>Experiments show that single-reward RLAIF selectively improves its targeted metric, while joint multi-reward training yields consistent gains across semantic quality and audio naturalness.
  arXiv  Detail & Related papers  (2026-01-27T00:55:14Z)
• AHELM: A Holistic Evaluation of Audio-Language Models [78.20477815156484]
  multimodal audio-language models (ALMs) take interleaved audio and text as input and output text.<n>AHELM is a benchmark that aggregates various datasets -- including 2 new synthetic audio-text datasets called PARADE and CoRe-Bench.<n>We also standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models.
  arXiv  Detail & Related papers  (2025-08-29T07:40:39Z)
• AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation [55.607230723223346]
  This work presents a systematic study of Large Audio Model (LAM) as a Judge, AudioJudge, investigating whether it can provide a unified evaluation framework that addresses both challenges.<n>We explore AudioJudge across audio characteristic detection tasks, including pronunciation, speaking rate, speaker identification and speech quality, and system-level human preference simulation for automated benchmarking.<n>We introduce a multi-aspect ensemble AudioJudge to enable general-purpose multi-aspect audio evaluation. This method decomposes speech assessment into specialized judges for lexical content, speech quality, and paralinguistic features, achieving up to 0.91 Spearman correlation with human preferences on
  arXiv  Detail & Related papers  (2025-07-17T00:39:18Z)
• Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark.<n>It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content.<n>It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• Speechworthy Instruction-tuned Language Models [71.8586707840169]
  We show that both prompting and preference learning increase the speech-suitability of popular instruction-tuned LLMs. We share lexical, syntactical, and qualitative analyses to showcase how each method contributes to improving the speech-suitability of generated responses.
  arXiv  Detail & Related papers  (2024-09-23T02:34:42Z)
• MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition [62.89464258519723]
  We propose a multi-layer cross-attention fusion based AVSR approach that promotes representation of each modality by fusing them at different levels of audio/visual encoders. Our proposed approach surpasses the first-place system, establishing a new SOTA cpCER of 29.13% on this dataset.
  arXiv  Detail & Related papers  (2024-01-07T08:59:32Z)
• FALL-E: A Foley Sound Synthesis Model and Strategies [0.5599792629509229]
  The FALL-E model employs a cascaded approach comprising low-resolution spectrogram generation, spectrogram super-resolution, and a vocoder. We conditioned the model with dataset-specific texts, enabling it to learn sound quality and recording environment based on text input.
  arXiv  Detail & Related papers  (2023-06-16T12:44:10Z)
• Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study [11.825240267691209]
  This paper investigates the potential of transferring high-level voice representations extracted from a public speaker dataset to enrich an acoustic event detection pipeline. We develop a dual-branch neural network architecture for the joint learning of voice and acoustic features during an AED process.
  arXiv  Detail & Related papers  (2021-10-07T04:03:21Z)
• Exploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models [37.60722440434528]
  In this paper, a novel model for audio quality assessment is proposed by jointly using bidirectional long short-term memory and an attention mechanism. The former is to mimic a human auditory perception ability to learn information from a recording, and the latter is to further discriminate interferences from desired signals by highlighting target related features. To evaluate our proposed approach, the TIMIT dataset is used and augmented by mixing with various natural sounds.
  arXiv  Detail & Related papers  (2020-05-16T17:54:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.