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Mingyue Huo, Yiwen Shao, Yuheng Zhang

We present TagSpeech, a unified LLM-based framework that utilizes Temporal Anchor Grounding for joint multi-speaker ASR and diarization. The framework is built on two key designs: (1) decoupled semantic and speaker streams fine-tuned via Serialized Output Training (SOT) to learn turn-taking dynamics; and (2) an interleaved time anchor mechanism that not only supports fine-grained timestamp prediction but also acts as a synchronization signal between semantic understanding and speaker tracking. Compared to previous works that primarily focus on speaker-attributed ASR or implicit diarization, TagSpeech addresses the challenge of fine-grained speaker-content alignment and explicitly models who spoke what and when in an end-to-end manner. Experiments on AMI and AliMeeting benchmarks demonstrate that our method achieves consistent improvements in Diarization Error Rate (DER) over strong end-to-end baselines, including Qwen-Omni and Gemini, particularly in handling complex speech overlaps. Moreover, TagSpeech employs a parameter-efficient training paradigm in which the LLM backbone is frozen and only lightweight projectors are trained, resulting in strong performance with low computational cost

Tarikul Islam Tamiti, Tonmoy Das, Nursadul Mamun, Anomadarshi Barua

We design CIS-BWE, a novel adversarial Bandwidth Extension (BWE) framework that introduces two chaos-informed discriminators - Multi-Resolution Lyapunov Discriminator (MRLD) and Multi-Scale Detrended Fractal Analysis Discriminator (MSDFA) - for capturing the deterministic chaos from speech. MRLD exploits Lyapunov exponents to capture nonlinear chaotic fluctuations. MSDFA exploits detrended fluctuation analysis to quantify fractal-like, long-range temporal chaotic correlations. To the best of our knowledge, MRLD and MSDFA are included here for the first time with a complex-valued adversarial network to explore the chaotic study of speech reconstruction. We also introduce a novel complex-valued and dual-stream generator, which uses our newly proposed ConformerNeXt as a core block with Lattice interactions, acting as a gating mechanism by enabling controlled mixing of information across streams. We extensively optimize our design across five resolutions and use depth-wise separable convolutions to make our model lightweight yet powerful. Our CIS-BWE is tested with a de facto English and French dataset for clean and noisy speech for generalization. It achieves better performance across a total of nine subjective and objective evaluation metrics with a 40x reduction in discriminator size and overall 0.5x fewer parameters, establishing a new baseline in the BWE task.

Zhejun Zhang, Wenqing Zhou, Haozhe Xu, Lin Zhang, Lei Li

Brain-tuning enhances brain alignment and downstream performance by fine-tuning speech language models with neural recordings. However, previous work relies primarily on fMRI, whose temporal resolution integrates neural activity over seconds, blending distinct processing stages into a single supervision signal and precluding temporally targeted training. We introduce ECoG-tuning, which leverages electrocorticography’s millisecond precision to train speech language models. We design temporally targeted windows—a speech window capturing acoustic-phonetic encoding and a language window capturing higher-order linguistic processing—grounded in neuroscientific findings about temporal encoding hierarchies. Evaluating three models on the Podcast ECoG dataset, we find that ECoG-tuning significantly improves brain alignment over pretrained and distillation baselines. Notably, full spatiotemporal dynamics yield 7–17% higher alignment than time-averaged supervision across models, and language-window tuning produces larger gains in higher-order language regions, indicating that temporal precision provides additional training value. Moreover, ECoG-tuned models consistently improve or maintain downstream performance. Overall, our work provides initial evidence that electrophysiology is a viable brain-tuning modality, demonstrating how neuroscientific insights into processing hierarchies can inform principled model training strategies. Code is available at [https://github.com/Mochizuki-BUPT/ECoG-Tuning-main](https://github.com/Mochizuki-BUPT/ECoG-Tuning-main).

Atnafu Lambebo Tonja, Srija Anand, Emilio Villa-Cueva, Israel Abebe Azime, Jesujoba Oluwadara Alabi, Muhidin A. Mohamed, Debela Desalegn Yadeta, Negasi Haile Abadi, Abigail Oppong, Nnaemeka Casmir Obiefuna 等

Africa is home to over one-third of the world’s languages, yet remains severely underrepresented in multimodal AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. The benchmark offers parallel text and speech modalities and was entirely created by native speakers. We find that models show poor performance across evaluated cultures, with near-zero accuracy on open-ended VQA when queried through native language or speech. To test linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the pressing need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. We release Afri-MCQA to support more inclusive multimodal AI development.

Hyunjung Joo, GyeongTaek Lee

The intonational structure of Seoul Korean has been defined with discrete tonal categories within the Autosegmental-Metrical model of intonational phonology. However, it is challenging to map continuous F_0 contours to these invariant categories due to variable F_0 realizations in real-world speech. Our paper proposes Dual-Glob, a deep supervised contrastive learning framework to robustly classify fine-grained pitch accent patterns in Seoul Korean. Unlike conventional local predictive models, our approach captures holistic F_0 contour shapes by enforcing structural consistency between clean and augmented views in a shared latent space. To this aim, we introduce the first large-scale benchmark dataset, consisting of manually annotated 10,093 Accentual Phrases in Seoul Korean. Experimental results show that our Dual-Glob significantly outperforms strong baseline models with state-of-the-art accuracy (77.75%) and F1-score (51.54%). Therefore, our work supports AM-based intonational phonology using data-driven methodology, showing that deep contrastive learning effectively captures holistic structural features of continuous F_0 contours.

Guanqun Bi, Zhoufu Liu, Zhuang Chen, Dazhen Wan, Xiyao Xiao, Minlie Huang

Psychiatric interviewing is a strategic, goal-oriented interaction that requires proactively steering the conversation to elicit latent information. However, existing methods often degenerate into rigid interrogation or aimless chitchat due to a lack of strategic planning. In this work, we introduce S4, a comprehensive framework grounded in Speech Act Theory, modeling the interview as a unified process of internal strategy (Illocution and Perlocution) and external realization (Locution). We synthesize a large-scale dataset with fine-grained psychiatric speech act annotations. Trained on this data, S4Dial employs reinforcement learning driven by long-term therapeutic effects to optimize the strategic chaining of atomic acts, aiming to maximally elicit information and maintain patient engagement. Experiments demonstrate that S4 significantly outperforms baselines, validating the effectiveness of our effect-driven strategic modeling.

Feiyu Zhao, Yiming Chen, Wenhuan Lu, Daipeng Zhang, Xianghu Yue, Jianguo Wei

Large Audio-Language Models (LALMs) have recently achieved strong performance across various audio-centric tasks. However, hallucination, where models generate responses that are semantically incorrect or acoustically unsupported, remains largely underexplored in the audio domain. Existing hallucination benchmarks mainly focus on text or vision, while the few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth. We therefore introduce HalluAudio, the first large-scale benchmark for evaluating hallucinations across speech, environmental sound, and music. HalluAudio comprises over 5K human-verified QA pairs and spans diverse task types, including binary judgments, multi-choice reasoning, attribute verification, and open-ended QA. To systematically induce hallucinations, we design adversarial prompts and mixed-audio conditions. Beyond accuracy, our evaluation protocol measures hallucination rate, yes/no bias, error-type analysis, and refusal rate, enabling a fine-grained analysis of LALM failure modes. We benchmark a broad range of open-source and proprietary models, providing the first large-scale comparison across speech, sound, and music. Our results reveal significant deficiencies in acoustic grounding, temporal reasoning, and music attribute understanding, underscoring the need for reliable and robust LALMs.

Jun-Hak Yun, Seung-Bin Kim, Seong-Whan Lee

Recent advancements in text-guided audio generation have yielded promising results in diverse domains, including sound effects, speech, and music. However, jointly generating speech with environmental audio remains challenging due to the inherent disparities in their acoustic patterns and temporal dynamics. We propose ImmersiveTTS, an environment-aware text-to-speech (TTS) model that generates natural speech seamlessly integrated within environmental contexts by explicitly modeling cross-modal interactions. Our model builds on a multimodal diffusion transformer and fuses transcript-aligned speech latent with text-conditioned environmental context via joint attention. To enhance semantic consistency, we introduce a domain-specific representation alignment objective tailored to environment-aware TTS, leveraging complementary self-supervised representations from speech and audio encoders. Experimental results show that ImmersiveTTS achieves higher naturalness, intelligibility, and audio fidelity than existing approaches across objective metrics and human listening tests.

Zhuoyue Gao, Xiaohui Wang, Xiaocui Yang, Wen Zhang, Daling Wang, Shi Feng, Yifei Zhang

Empathetic speech dialogue requires not only understanding linguistic content but also perceiving rich paralinguistic information such as prosody, tone, and emotional intensity for affective understandings. Existing speech-to-speech large language models either rely on ASR transcription or use encoders to extract latent representations, often weakening affective information and contextual coherence in multi-turn dialogues. To address this, we propose ES4R, a framework for speech-based empathetic response generation. Our core innovation lies in explicitly modeling structured affective context before speech encoding, rather than relying on implicit learning by the encoder or explicit emotion supervision. Specifically, we introduce a dual-level attention mechanism to capture turn-level affective states and dialogue-level affective dynamics. The resulting affective representations are then integrated with textual semantics through speech-guided cross-modal attention to generate empathetic responses. For speech output, we employ energy-based strategy selection and style fusion to achieve empathetic speech synthesis. ES4R consistently outperforms strong baselines in both automatic and human evaluations and remains robust across different Large Language Model (LLM) backbones. Code: https://github.com/Bean0901/ES4R.

Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu 等

End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks primarily evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored. We introduce Audio MultiChallenge an open-source benchmark to evaluate these systems under natural multi-turn interaction patterns. Building on the text-based MultiChallenge framework, which evaluates Inference Memory, Instruction Retention, and Self Coherence, we introduce a new axis Voice Editing that tests robustness to mid-utterance speech repairs and backtracking. We augment each axis to the audio modality, such as introducing Audio-Cue challenges for Inference Memory that require recalling ambient sounds and paralinguistic signals beyond semantic content. We curate 452 conversations from 47 speakers with 1,712 instance-specific rubrics through a hybrid pipeline that exposes model failures at scale while preserving natural disfluencies found in unscripted human speech. Our evaluation reveals that even frontier models struggle on our benchmark, with our highest-performing model achieving a 54.65% pass rate. Error analysis shows that models are not sufficiently robust to human speech when tracking instructions, edits, and audio cues, highlighting the need for improved audio-native multi-turn interaction capabilities.

Nanjie Li, Xiaoyong Guo, Hao Huang, Xu Haihua, Wei Shi

Neural transducers offer an alignment-free framework for speech-to-text modeling, and hierarchical transducer architectures further improve multilingual joint automatic speech recognition (ASR) and speech translation (ST) by stacking a translation-focused encoder on top of an ASR encoder. However, extending hierarchical transducers to multilingual many-to-many settings remains challenging: fully shared models often suffer from negative transfer and unstable target-language generation, while training separate models for each direction is computationally prohibitive. We propose LCMA-SRT (Language-Conditional Mixture-of-Experts Adapters for Speech Recognition and Translation), which augments a hierarchical transducer with language-conditional Mixture-of-Experts (MoE) adapters. A source-conditioned MoE adapter (SRC-MoE) uses source-language embeddings to reduce cross-language interference and improve multilingual ASR. A target-conditioned MoE adapter (TGT-MoE) uses the desired target language to reduce cross-target interference and stabilize target-language generation in many-to-many ST. Experiments on Europarl-ST (9 languages, 72 directions) show that LCMA-SRT improves both ASR and ST within a single joint model, reducing average WER and improving BLEU and COMET over strong hierarchical transducer baselines. We release our code and models at https://github.com/linanjie0820/LCMA-SRT.

Feng Jiang, Zhiyu Lin, Yiyang Liu, Liumeng Xue, Fan Bu, Yuhao Du, Xiangying Chen, Benyou Wang, Haizhou Li

Recent advances in large language models (LLMs) have fundamentally reshaped speech-to-speech (S2S) systems, enabling increasingly natural spoken interaction. However, existing benchmarks still rely heavily on text-based evaluation and largely ignore paralinguistic cues such as prosody, emotion, and speaker traits, which are central to expressive and human-like communication. We introduce S2S-Arena, a speech-native benchmark for evaluating instruction-following S2S models with explicit assessment of both semantic understanding and paralinguistic expression. S2S-Arena features a four-level interaction protocol that systematically probes models under increasing paralinguistic complexity, a two-stage data construction pipeline that produces 1,243 speech samples spanning 100+ real-world tasks, and an arena-style evaluation framework that enables reference-free, pairwise comparison directly in the speech modality. Benchmarking 10 state-of-the-art S2S systems over 1,000+ comparisons reveals substantial performance gaps (especially under complex paralinguistic demands) between current academic and industrial systems. Our analysis further identifies key design factors governing expressive instruction following, providing actionable insights for building more natural, robust, and human-aligned speech agents.

Sanjeevan Selvaganapathy, Mehwish Nasim

We investigate the efficacy of Large Language Models (LLMs) in detecting implicit and explicit hate speech, examining how models with minimal safety alignment (uncensored) compare with more heavily aligned (censored) counterparts in a deployed-model setting when deployed using political personas. While uncensored models are often framed as offering a less constrained perspective, our results reveal a trade-off: censored models outperform their uncensored counterparts in both accuracy and robustness, achieving 69.0% versus 64.1% strict accuracy. However, this higher performance is also associated with greater resistance to persona-based influence, while uncensored models are more malleable to ideological framing. Furthermore, we identify critical failures across all models in understanding nuanced language such as irony. We also find alarming fairness disparities in performance across different targeted groups and systemic overconfidence that renders self-reported certainty unreliable. These findings challenge the notion of LLMs as objective arbiters and highlight the need for more sophisticated auditing frameworks that account for fairness, calibration, and ideological consistency. Taken together, these results point to censorship-as-deployed rather than safety alignment in isolation as the more appropriate frame for interpreting model differences.

Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu

Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored. Crucially, there is no consensus on whether audio–language models can build effective general-purpose audio encoders, nor a systematic understanding of how pretraining objectives behave across diverse tasks and scales.We identify three key barriers: limited scale of audio-text corpora, limited coverage of audio attributes in existing caption corpora, and lack of systematic exploration and evaluation.To fill this gap, we present the first principled empirical study of ALP.We first introduce CaptionStew, a 10.7M caption dataset aggregating open-source audio-text corpora across multiple domains and captioning focuses.We then conduct the first comprehensive evaluation comparing contrastive and captioning objectives for learning audio representation across speech, music, and environmental sound tasks.Our results not only demonstrate that ALP yields competitive, transferable representations, but reveal critical trade-offs: contrastive learning offers superior data efficiency, while captioning exhibits better scalability.Furthermore, we find that the benefits of supervised initialization often diminish at larger scales, challenging common practices.By grounding these claims in empirical evidence, we establish a viable pathway toward general-purpose audio representation learning, guiding future research.

Md Arid Hasan, Firoj Alam, Md Fahad Hossain, Usman Naseem, Syed Ishtiaque Ahmed

Online social media platforms have become central to communication and information exchange, however, they also serve as fertile ground for hate speech, offensive language, and bullying targeting individuals and communities. Such content undermines online safety and inclusion, underscoring the need for reliable detection systems—especially in low-resource languages with limited moderation tools. For Bangla, existing work provides valuable resources and models, however, they are mostly single-task (e.g., binary hate/offense) with narrow coverage of key dimensions such as type, severity, and target. We address these gaps by introducing *the first multi-task* Bangla hate-speech dataset, *BanglaMultiHate*, one of the largest manually annotated dataset to date. Using this resource, we performed a comparative study across different baselines, monolingual pretrained models, and LLMs under zero-shot, few-shot, and LoRA fine-tuning settings. Our findings show that while LoRA-tuned LLMs rival BanglaBERT, culturally grounded pretraining remains crucial for robust performance. Overall, *BanglaMultiHate* establishes a stronger benchmark for hate speech detection in low-resource contexts. All data and scripts are released for reproducibility.

Santosh T.Y.S.S

Spoken language models (SpeechLMs) are increasingly real-time conversational actors. Yet many culturally consequential aspects of spoken interaction are not primarily lexical. Across sociolinguistics, linguistic anthropology, and conversation analysis, meaning emerges through how talk is produced and coordinated—prosody, timing, turn-taking, overlap, backchannels, and repair—within situated speech events. A transcript can be semantically correct yet interactionally inappropriate because many culture-bearing signals are audible and sequential rather than textual. This position paper argues for a speech-first view of cultural competence as interactional competence: the ability of a spoken agent to participate appropriately in event-situated interaction with locally normative conduct, while allowing plural acceptable realizations. Here, appropriate does not imply generic human-likeness; in many applications, the desired behavior may instead be constrained, neutral, predictable, or tool-like under an application-specific interaction contract. We synthesize social-science foundations into a theory-derived taxonomy of culture-bearing signals in speech, identify interactional phenomena where transcript correctness fails to predict appropriateness, and ground the agenda in today’s SpeechLM stacks and evaluation practice. We propose an evaluation framing that complements WER/MOS and broad capability suites by making speech events and interaction contracts explicit, diagnosing where modern pipelines lose interactional cues, and treating cultural appropriateness as a norm-conditioned target rather than generic “naturalness.”

Giulio Zhou, Tsz Kin Lam, Alexandra Birch, Barry Haddow

Prosodic features such as pitch, timing, and intonation are central to spoken communication, conveying emotion, intent, and discourse structure. In text-based settings, where these cues are absent, emojis act as visual surrogates that add affective and pragmatic nuance. This study examines how emojis influence prosodic realisation in speech and how listeners interpret prosodic cues to recover emoji meanings. Unlike previous work, we directly link prosody and emojis by analysing human speech data collected through a controlled elicited production task. Using Bayesian multilevel modelling, we show that speakers systematically adapt their prosody based on emoji cues, and that listeners can recover intended meanings significantly above chance. Furthermore, our results reveal a clear hierarchy in prosodic shifts: greater semantic differences between emojis correspond to increased prosodic divergence. These findings suggest that emojis are meaningful carriers of prosodic intent that bridge the gap between digital text and spoken production.

YuFei Shi, Qian Chen, Wen Wang, Xiangang Li, Zhen-Hua Ling, Yang Ai

We propose UniVocal, a unified framework that implicitly infers vocal modes from text context to pioneer Speech-Singing Code-Switching (SCS) Synthesis—a task where transitions are autonomously driven by textual semantics, akin to seamless human language blending. Unlike single-mode generation or systems relying on switching-control tags, our proposed UniVocal implicitly infers vocal modes solely from text context. To achieve this, we employ a data-efficient two-stage curriculum learning strategy that progressively trains a competitive TTS system to acquire the desired SCS capability. Addressing data scarcity, we introduce a scalable pipeline to synthesize diverse code-switching data that is both semantically and acoustically natural, alongside a new multi-scenario benchmark, SCSBench. To address limitations of semantic tokenizers in capturing acoustic details, we also introduce refined cent token and Chain-of-Thought (CoT) generation for planning prosody before content generation, effectively enhancing empathetic speech generation and singing melody. Experimental results demonstrate that UniVocal achieves state-of-the-art performance on SCSBench while maintaining competitive performance on regular speech and singing tasks. Audio samples are available at https://project-univocal-demo.github.io/demo/. The code and dataset are released at https://github.com/FunAudioLLM/FunResearch/tree/main/UniVocal.

Sirry Chen, Jieyi Wang, Wei Chen, Zhongyu Wei

Medical consultations are intrinsically speech-centric. However, most prior works focus on long-text-based interactions, which are cumbersome and patient-unfriendly. Recent advances in speech language models (SpeechLMs) have enabled more natural speech-based interaction, yet the scarcity of medical speech data and the inefficiency of directly fine-tuning on speech data jointly hinder the adoption of SpeechLMs in medical consultation. In this paper, we propose SpeechMedAssist, a SpeechLM natively capable of conducting speech-based multi-turn interactions with patients. By exploiting the architectural properties of SpeechLMs, we decouple the conventional one-stage training into a two-stage paradigm consisting of **(1) Knowledge Capability Injection via Text** and **(2) Modality Re-alignment with Limited Speech Data**, thereby reducing the requirement for medical speech data to only **10k** synthesized samples. To evaluate SpeechLMs for medical consultation scenarios, we design a benchmark comprising both single-turn question answering and multi-turn simulated interactions. Experimental results show that our model outperforms all baselines in both effectiveness and robustness in most evaluation settings.

Priyanshu Mahato, Aniket Santosh Mishra, Kripabandhu Ghosh

While Large Language Models (LLMs) are frequently cited for their sophisticated pragmatic reasoning (CITATION), recent progress in sarcasm detection increasingly relies on synthetic benchmarks (CITATION). This study exposes a catastrophic generalization gap in this paradigm: we observe that models achieve near-perfect accuracy on synthetic data but collapse to random guessing on organic human speech. By triangulating hidden state geometry, entropy analysis, and causal interventions, we demonstrate that this disparity stems from shortcut learning (CITATION)—models exploit the low-entropy statistical signatures of generated text while remaining “semantically blind” to the pragmatic cues essential for irony. Our findings indicate that high performance on synthetic leaderboards reflects forensic pattern matching rather than the genuine linguistic intelligence assumed in prior work, creating a statistical mirage of competence.