The paper introduces EmoStance, a method for controlling the affective orientation of empathetic responses in dialogue systems. It leverages weak supervision from multi‑annotator emoji distributions to create a latent control space that approximates listener stance, and uses a frozen instruction‑tuned LLM steered by continuous prefix embeddings. Evaluation shows a 62.2% decisive win rate over baselines, especially in contextual specificity and perceived responsiveness.
arXiv:2609.38157v1 Announce Type: cross
Abstract: Emotion-conditioned text-to-speech (TTS) models may fail to express the requested emotion reliably, and improving controllability by additional train...
By Kuan-Po Huang, Haohe Liu, Puyuan Peng, Haibin Wu, Zhaoheng Ni, Hung-yi Lee, Jinwon Lee, Neha Chachra
The paper audits a multilingual affective generation benchmark that uses emoji summaries for Bangla, English, and Hindi sentences. It finds that the benchmark’s conclusions are largely artifacts of the measurement instrument, with no system significantly outperforming another when annotators are treated as random factors. The study shows that annotator identity and output length drive most variance, and proposes a new stable metric called emoji‑affect decodability.
By Fardeen Sadab, Adib Sakhawat
arXiv:2608.30325v1 Announce Type: new
Abstract: Natural-language instructions enable flexible control of synthesized speech, yet emotional TTS systems primarily model a single utterance-level affect,...
By Yan Zhou, Yun Hong, Yang Feng
arXiv:2609.38182v1 Announce Type: cross
Abstract: Avatar-based multimodal empathetic response generation has emerged as a pivotal capability in human-centric systems, aiming to recognize user emotion...
By Xiaolin Chen, Xuemeng Song, Jinlan Fu, Weili Guan, Mong-Li Lee, Wynne Hsu
arXiv:2609.15089v1 Announce Type: new
Abstract: Empathetic response generation in spoken dialogue systems requires both accurate emotion perception and appropriate emotion regulation. Grounded in psy...
By Hongyu Jin, Wenda Zhang, Runqiu Fei, Gongping Huang, Mike Conway, Ting Dang
arXiv:2608.29035v1 Announce Type: new
Abstract: Emotion recognition in conversations is increasingly tackled with language models, but these models can be unstable and expensive to fine-tune or to pr...
By Thao Le, Michael Thielscher
The paper introduces EmoVec, a lightweight framework that enables controllable affective generation in large language models by steering latent vectors. EmoVec identifies emotion-specific directions from paired neutral and emotion-conditioned responses using contrastive activation addition, then refines these directions through task-specific debiasing and principal subspace removal. During inference, the refined vectors are injected into the final residual stream with static or scenario-adaptive scaling, allowing continuous control over emotional intensity without updating model weights, and experiments across three LLMs and eight emotions demonstrate improved emotional salience while preserving semantic content, fluency, and coherence.
By Xixian Yong, Siyuan Chang, Yingying Zhang, Xian Wu, Xiao Zhou
arXiv:2609.05806v1 Announce Type: new
Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
By Amir Ben Khalifa, Fanny Bezancon, Amine Trabelsi, Bessam Abdulrazak
The paper introduces a discriminative adaptation for SpeechLLMs that reads the hidden state of the final prompt token via a simple classification head, enabling emotion recognition in a single forward pass without altering the backbone. This approach replaces the generative decoder, which can produce out‑of‑set labels and favor frequent classes, with a controlled comparison between generative and discriminative inference. Experiments on IEMOCAP show improved Macro F1 scores, elimination of hallucinations, and larger gains on realistic ASR transcripts, while revealing that emotion directions encode indirect associations reflecting web‑scale text biases.
By Hasindri Watawana, Sergio Burdisso, Esa\'u Villatoro-Tello, Manjunath K E, Kadri Hacioglu, Petr Motlicek, Andreas Stolcke
arXiv:2602. 03420v2 Announce Type: replace-cross Abstract: Emotional expression in human speech is nuanced and compositional, often involving multiple, sometimes conflicting, affective cues that may diverge from linguistic content.
By Siyi Wang, Shihong Tan, Siyi Liu, Hong Jia, Gongping Huang, James Bailey, Ting Dang
EmoTra‑TTS introduces a method for smooth intra‑utterance emotion transitions in speech synthesis. It uses a multi‑pass flow blending pipeline, dual‑stage VAD conditioning, and direction‑magnitude decoupled injection to generate frame‑aligned emotional prosody. The system adds only 0.43% more parameters, incurs no latency, and outperforms four state‑of‑the‑art baselines and two commercial systems in emotion transition quality and overall preference tests.
By Tianchi Liu, Zeyang Song, Tianrui Wang, Zhipeng Li, Chenglin Xu, Yiwen Guo