3arXiv cs.CL (Computation and Language)·
LuxEmo: 21-hour expressive speech corpus for Luxembourgish TTS with emotion categories
Researchers introduce LuxEmo, a 21-hour conversational speech corpus for Luxembourgish derived from RTL youth broadcasts, annotated with four emotion categories via a semi-automatic curation pipeline combining VAD, denoising, language ID, and human validation. The paper benchmarks five expressive TTS systems across cross-lingual transfer, multilingual support, and prosody transfer approaches. The work addresses the underrepresentation of low-resource languages in speech technology research.
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SpeechEQ benchmark evaluates emotional intelligence in speech-language models across 15 EQ subscales
Researchers introduce SpeechEQ, a benchmark framework for evaluating sociolinguistic and emotional reasoning in Speech-Language Models (SLMs), comprising 2,265 multi-turn dialogues across 15 Emotional Quotient subscales grounded in EQ-i 2.0 theory. The benchmark reveals three systematic failure modes in current multimodal models: over-reliance on text (modality shortcut), alignment-induced safety trap, and contextual amnesia across turns. End-to-end architectures outperform cascaded systems but all evaluated models fall short of genuine emotional awareness. The dataset and demo are publicly released on HuggingFace.
Multimodal Pathos Analysis in Political Speech: LLM-Based vs. Acoustic Emotion Models
Researchers compare acoustic speech emotion recognition (emotion2vec_plus_large), multimodal LLM analysis (Gemini 2.5 Flash), and a multi-agent LLM ensemble (TRUST pipeline) for detecting Pathos in a Bundestag political speech. Gemini Valence correlates strongly with TRUST-Pathos scores (rho=+0.664) while acoustic Valence does not (rho=+0.097), suggesting LLMs capture semantically defined political emotion far better than acoustic models. The study also critiques standard SER benchmark corpora (EMO-DB) for acted speech, cultural bias, and category incompatibility. Results indicate acoustic features remain useful for low-level arousal estimation but are insufficient proxies for rhetorical-emotional analysis.
HPRO: Hierarchical Progressive Reward Optimization for Emotional Text-to-Speech
Researchers propose HPRO, a hierarchical progressive reward optimization framework for LLM-based Text-to-Speech systems that improves emotional expressiveness. The approach introduces HD-Emo codec as a differentiable reward model that separates content and style preference tokens to avoid conflicting gradients, and bridges sentence-level and frame-level reward signals through progressive multi-scale alignment. Experiments show improved emotional expressiveness while preserving linguistic intelligibility.
LexNeo-Bench: Probing LLM Knowledge of Lexical Borrowing in Luxembourgish via Knowledge-Graph Prompting
Researchers introduce LexNeo-Bench, a 3,050-instance benchmark for evaluating LLM performance on lexical borrowing classification and neology detection in Luxembourgish, a low-resource contact language. Three multilingual LLMs are tested across 34 prompt configurations; without external context, models perform near chance on borrowing classification (25–35%). Injecting instance-specific subgraphs from a linguistic knowledge graph raises accuracy to 71–81% and largely closes the gap between small and large models, though neology detection remains difficult. The study highlights the value of lexicon-aware, structured prompting for low-resource multilingual evaluation.
Emotion vectors replicated in open-weight LLMs with architecture-dependent valence geometry
A new arXiv preprint extends prior findings on emotion vectors in Claude Sonnet 4.5 to two open-weight models, Apertus-8B-Instruct-2509 and Gemma-4-E4B-it, by extracting emotion contrast vectors across all layers. The authors recover valence geometry in both models (peak PC1-valence correlations of r=0.76 and r=0.83, near Claude's r=0.81) but find notable architectural differences: Gemma encodes valence strongly in early layers while Apertus shows the opposite pattern. Arousal encoding proves sensitive to the corpus used for extraction, suggesting uneven distribution of arousal-relevant cues across model-generated text.
EMPATH: Multilingual multi-turn safety benchmark for emotional-support chatbots reveals score inflation and run-to-run reliability failures
EMPATH is a new arXiv benchmark for evaluating the safety of emotional-support chatbots, using an auditor model to generate multi-turn crisis conversations and a calibrated judge model to score transcripts across 19 metrics in five dimensions. Built for Mexican Spanish and US English, the benchmark surfaces score inflation on 10 of 19 metrics under uncalibrated rubrics and finds that run-to-run reliability is a per-model safety property: one model swings 2–10 points on a crisis metric across identical reruns, and DeepSeek V4 Pro produces different conversations at temperature 0. Evaluation of three frontier models shows aggregate scores within 0.74 points but per-metric divergences up to six points, with rankings stable across a cross-family judge at 93% within ±1.
Synthetic LLM-generated conversations improve ASR training for low-resource languages
Researchers propose a pipeline that uses LLMs to generate scenario-level dialogues and TTS to synthesize multi-speaker audio, creating simulated conversational training data for ASR systems. Evaluated on the Hungarian BEA-Dialogue benchmark, a model trained on 67 hours of real plus 636 hours of synthetic data outperforms a zero-shot model trained on 2,700 hours of real Hungarian speech. The study tests five LLM families under multiple budget and mixing configurations using a FastConformer-Large backbone, finding that generator choice and data composition significantly affect gains.
LLUMI: Fine-Tuning Open-Source LLMs for Mental Health Writing Assistance Using Reddit Community Feedback
LLUMI is a two-component system (a generation model and an improvement model) designed to provide mental health writing assistance using smaller open-source LLMs hosted in privacy-preserving, on-premise environments. The system leverages Reddit community endorsement signals (upvotes/downvotes) to construct preference pairs for SFT and DPO training, then further aligns outputs via human evaluation across readability, empathy, connection, actionability, and safety dimensions. Results show LLUMI achieves performance comparable to proprietary GPT-based models on linguistic and human evaluations, suggesting community-derived preference signals can substitute for expensive expert labeling in sensitive domains.