Decoding Ambiguous Emotions with Test-Time Scaling in Audio-Language Models

• URL: http://arxiv.org/abs/2602.03873v1
• Date: Sun, 01 Feb 2026 07:41:57 GMT
• Title: Decoding Ambiguous Emotions with Test-Time Scaling in Audio-Language Models
• Authors: Hong Jia, Weibin Li, Jingyao Wu, Xiaofeng Yu, Yan Gao, Jintao Cheng, Xiaoyu Tang, Feng Xia, Ting Dang,
• Abstract summary: We introduce the first benchmark for ambiguous emotion recognition in speech with ALMs under test-time scaling.<n>Our evaluation systematically compares eight state-of-the-art ALMs and five TTS strategies across three prominent speech emotion datasets.<n>Our benchmark establishes a foundation for developing more robust, context-aware, and emotionally intelligent speech-based AI systems.
• Score: 18.059483722792077
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Emotion recognition from human speech is a critical enabler for socially aware conversational AI. However, while most prior work frames emotion recognition as a categorical classification problem, real-world affective states are often ambiguous, overlapping, and context-dependent, posing significant challenges for both annotation and automatic modeling. Recent large-scale audio language models (ALMs) offer new opportunities for nuanced affective reasoning without explicit emotion supervision, but their capacity to handle ambiguous emotions remains underexplored. At the same time, advances in inference-time techniques such as test-time scaling (TTS) have shown promise for improving generalization and adaptability in hard NLP tasks, but their relevance to affective computing is still largely unknown. In this work, we introduce the first benchmark for ambiguous emotion recognition in speech with ALMs under test-time scaling. Our evaluation systematically compares eight state-of-the-art ALMs and five TTS strategies across three prominent speech emotion datasets. We further provide an in-depth analysis of the interaction between model capacity, TTS, and affective ambiguity, offering new insights into the computational and representational challenges of ambiguous emotion understanding. Our benchmark establishes a foundation for developing more robust, context-aware, and emotionally intelligent speech-based AI systems, and highlights key future directions for bridging the gap between model assumptions and the complexity of real-world human emotion.

Related papers

• ES4R: Speech Encoding Based on Prepositive Affective Modeling for Empathetic Response Generation [30.006550552714938]
  Empathetic speech dialogue requires not only understanding linguistic content but also perceiving rich paralinguistic information.<n>Existing speech-to-speech large language models either rely on ASR transcription or use encoders to extract latent representations.<n>We propose textbfES4R, a framework for speech-based empathetic response generation.
  arXiv  Detail & Related papers  (2026-01-16T10:26:50Z)
• The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era [95.35748535806744]
  We launch the first Human-like Spoken Dialogue Systems Challenge (HumDial) at ICASSP 2026.<n>This paper summarizes the dataset, track configurations, and the final results.
  arXiv  Detail & Related papers  (2026-01-09T06:32:30Z)
• A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction [50.05919688888947]
  This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
  arXiv  Detail & Related papers  (2026-01-08T14:07:30Z)
• MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models [108.61337743051483]
  We present MME-Emotion, a systematic benchmark that assesses both emotional understanding and reasoning capabilities of MLLMs.<n>MME-Emotion contains over 6,000 curated video clips with task-specific questioning-answering (QA) pairs, spanning broad scenarios to formulate eight emotional tasks.<n>It incorporates a holistic evaluation suite with hybrid metrics for emotion recognition and reasoning, analyzed through a multi-agent system framework.
  arXiv  Detail & Related papers  (2025-08-11T03:14:55Z)
• GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations [35.63053777817013]
  GatedxLSTM is a novel multimodal Emotion Recognition in Conversation (ERC) model.<n>It considers voice and transcripts of both the speaker and their conversational partner to identify the most influential sentences driving emotional shifts.<n>It achieves state-of-the-art (SOTA) performance among open-source methods in four-class emotion classification.
  arXiv  Detail & Related papers  (2025-03-26T18:46:18Z)
• MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis [53.012111671763776]
  This study introduces MEMO-Bench, a comprehensive benchmark consisting of 7,145 portraits, each depicting one of six different emotions. Results demonstrate that existing T2I models are more effective at generating positive emotions than negative ones. Although MLLMs show a certain degree of effectiveness in distinguishing and recognizing human emotions, they fall short of human-level accuracy.
  arXiv  Detail & Related papers  (2024-11-18T02:09:48Z)
• ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling [0.0]
  We present a novel solution by employing a mixture of experts, multiple encoders, to offer distinct perspectives on the emotional state of the user's utterance. We propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots.
  arXiv  Detail & Related papers  (2024-02-25T20:36:51Z)
• EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation [34.24557248359872]
  We propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e., Identity Masked Multi-Head Attention (IMMHA), Dialogue-based Gated Recurrent Unit (DiaGRU) and Skip-chain Conditional Random Field (SkipCRF) Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-20T13:58:35Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability [82.39099867188547]
  Emotional text-to-speech synthesis (ETTS) has seen much progress in recent years. We propose a new interactive training paradigm for ETTS, denoted as i-ETTS. We formulate an iterative training strategy with reinforcement learning to ensure the quality of i-ETTS optimization.
  arXiv  Detail & Related papers  (2021-04-03T13:52:47Z)

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.