Speech and dialogue systems

Neueste Paper

• AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models

  Yuxiang Wang, Kunyu Feng, Yuancheng Wang, Zihang Liu, Shengbo Cai, Qinke Ni, Wan Lin, Tao Feng, Yingda shen, Ming-Hao Hsu, Zhixian Zhao, Liqiang Zhang, Teddy Sun, Steve Yves, Zhizheng Wu · 2. Oktober 2026

  Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together. Explicit chain-of-thought (CoT) improves reasoning and audio understanding, but generating intermediate reasoning tokens delays responses. Describing f…

• Mixture of Decoders for Diverse Dialog Response Generation

  Wenchao Du · 2. Oktober 2026

  Mixture modeling is a long established machine learning technique for learning large sets of multi-modal data. While it is known that sequence-to-sequence models for dialog response generation suffer from the problem of low diversity, we hypothesize that it is because sequence-to-sequence models ten…

• Multi-Party Backchannel Prediction: a Diagnosis, a Benchmark, and a Ceiling

  Mohammed Hafsati, Ahmed Loughzali · 2. Oktober 2026

  Backchannel prediction has been studied almost entirely in dyadic conversation. We introduce a multi-party benchmark based on the AMI corpus, comprising 682 masked-listener views from 171 meetings, 190 speakers, and 18,697 backchannel events, with a person-disjoint held-out split. A state-of-the-art…

• DuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents

  Puneet Mathur, Nedim Lipka, Zeyu Jin, Dinesh Manocha · 2. Oktober 2026

  Voice agents enable low-latency, natural interaction, yet their ability to faithfully ground responses in external documents remains underexplored. We introduce DuplexSpeechBench-Document Grounding (DSB-DG), a benchmark for evaluating document grounding in voice agents across five professional domai…

• Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants

  Vaishnav Negi, Lev Sorokin, Soroosh Tayebi Arasteh, Andrea Stocco · 1. Oktober 2026

  In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. Ensuring their reliability is challenging due to multi-turn interactions, the absence of explicit ground truth, and strict safety constraints. Existin…

• FD-VAD: Semantic Endpoint Detection for Streaming Full-Duplex Speech

  Puneet Mathur, Dinesh Manocha · 1. Oktober 2026

  Natural turn-taking in full-duplex voice interaction requires determining from partial speech whether a pause reflects hesitation or a completed conversational intent. Acoustic voice activity detection lacks this semantic information, while cascaded ASR-based endpointing introduces transcription dep…

• SCB: SpeechConversationBench for Evaluating Multi-Turn Reasoning in Speech-to-Speech Models

  Kanpat Vesessook, Saksorn Ruangtanusak · 1. Oktober 2026

  Speech-to-speech systems must solve tasks whose requirements emerge across conversational turns. We introduce SpeechConversationBench (SCB), a focused evaluation of spoken mathematical reasoning using 103 sharded GSM8K problems. The framework compares the original problem delivered in one turn (full…

• Does Gradient Conflict Predict the Understanding--Generation Trade-off? A Controlled Audit of Conflict-Metric Validity in Unified Multimodal Models

  Shuyang Jiang, Fucheng Deng, Yuchuan Luo, Zhenyu Wu · 1. Oktober 2026

  Unified multimodal models (UMMs) are increasingly designed around gradient conflict between understanding and generation objectives. The premise that reducing these metrics improves the downstream understanding-generation trade-off has never been tested directly. We audit it in a controlled testbed,…

• Talk2Agent: Benchmarking Voice Interfaces for Text Agents

  Terumi Chiba, Guangzhi Sun, Zheqi Yuan, Chao Zhang · 1. Oktober 2026

  Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular interface for interacting with such systems. Speech input introduces an additional failure point: transcription errors can alter task-critical entities…

• Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks

  Sumit Asthana, Michael Ion, Kevyn Collins Thompson · 1. Oktober 2026

  High quality synthetic data is central to post training LLMs for adaptive AI applications that represent the diverse expert strategies and decisions in conversations. Prompting LLMs directly or conditioning them on end use scenarios yields low diversity data that collapses onto dominant modes. We pr…

• LLMs are General Asynchronous Agents

  George Yakushev, Denis Mazur, Vladimir Bartenev, Vyacheslav Zhdanovskiy, Timofey Byzov, Vladimir Kaurkin, Vadim Pastushenko · 30. September 2026

  Modern LLMs are increasingly capable as autonomous agents, but they follow sequential interaction cycles: read, think, reply or call tools, repeat. Many real-world use cases are not sequential: voice assistants, embodied agents, and monitoring systems receive new inputs while they think or perform a…

• Distributional Metrics for Evaluating Spoken Conversational Systems

  Shree Harsha Bokkahalli Satish, Erica Cooper, Patr\'icia Schmidtov\'a, Maike Z\"ufle, \'Eva Sz\'ekely, Nicholas Sanders, Ond\v{r}ej Klejch · 30. September 2026

  Evaluating conversational systems is a difficult and unresolved problem. We introduce the Conversational Distribution Score (CDS), which compares distributions of conversational behaviour using human conversations as a reference. CDS describes speech rate, syllabic rhythm, and turn interaction throu…

• Towards Communication-Efficient Social Intelligence in Language Agents

  Linxiao Gong, Yijie Xu, Tianfu Wang, Yin Wu, Yili Wang, Xingbo Yao, Huizai Yao, Xilin Xia, Haowen Yang, Hui Xiong · 30. September 2026

  Socially intelligent language agents must negotiate, coordinate, and resolve conflicting preferences while respecting the time and attention of both participants. Balancing these demands is challenging because agents must convey enough to address a partner's constraints and advance their goals witho…

• SALMONN-duo: Adaptive Dual-System Coordination for Full-Duplex Voice Agents

  Wenyi Yu, Siyin Wang, Terumi Chiba, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Chao Zhang · 30. September 2026

  Full-duplex speech large language models (LLMs) enable low-latency, natural voice interaction. However, real-world agents must also use tools and perform deliberative reasoning-operations whose variable latency and computational cost conflict with the stringent timing requirements of real-time conve…

• Understanding Clinical Cognitive Dialogues Using Large Language Models

  Vishalakshi Arumugam, Dan Schumacher, Veronica Rammouz, Enrique Gonzalez Guerrero, Jeremy Davis, Anthony Rios · 30. September 2026

  In-person cognitive assessment is both a test and an interaction. Clinicians explain tasks, repair misunderstandings, and adapt to patient responses, while patients may hesitate, seek clarification, or disengage. Yet clinical dialogue resources rarely label the interaction structure needed to study …

• Lost with a Map: Conversational State and Behavioral Reliability in Language Models

  Atahan Dokme, Larry Heck · 30. September 2026

  Task-oriented dialogue requires maintaining and updating information across turns, yet language models expose no explicit belief-state object. We study how conversational state is represented, updated, and used inside eight instruction-tuned language models from four families on MultiWOZ and SGD. St…

• Context Spanning: A Communication Framework for Full-Duplex Speech Models and External LLM Backends

  Seonghyeon Go, Yongwoo Kim, Hyeonjin Cha, Jaeho Shin · 30. September 2026

  Full-duplex spoken dialogue models can listen and speak simultaneously like the real-time dynamics of human conversation. For natural dialogue, the ability to search for external information in real-time is also an important capability. Many models remain trapped in parametric knowledge, leaving the…

• A model of rational interlocutors: Unification of comprehension and production

  Hanlin Wu, Zhenguang G. Cai · 30. September 2026

  Who we communicate with influences both our interpretation of their utterances and the design of our own. Such adjustment to the conversational partner is studied as speaker modeling in comprehension and as audience design in production, with the two literatures having developed largely separately. …

• IndicFDB: Benchmarking Full-Duplex Voice Agents across Indian Languages

  Rajarshi Roy, Shobhit Banga, Jonathan Raiman, Supriya Paul, Bhaskar Singh, Manmeet Kaur, Sagar Jain, Hanuman Sidh, Pranav Sharma, Aditya Singh, Aaditya Pareek, Manas Dhir, Adi Margolin, Niket Agarwal, Bryan Catanzaro · 30. September 2026

  Full-duplex voice agents must handle pauses, take turns, backchannel, and respond to user interruptions in real time. Full-Duplex-Bench evaluates these behaviors, but its English-only corpus and reliance on word-timestamped ASR and an English-prompted LLM judge make it difficult to extend to Indian …

• Thinking in Depth, Speaking Directly: Recurrent Latent Reasoning for Paralinguistically Grounded Spoken Dialogue

  Shengbo Cai, Yuxiang Wang, Jingran Xie, Zhisheng Zhang, Shun Lei, Di Cao, Teddy Sun, Zhiyong Wu · 30. September 2026

  Empathetic spoken dialogue requires models to use both what is said and how it is said to decide how to respond. Explicit CoT can improve paralinguistic perception and make acoustic cues more explicit in replies, yet does not ensure their effective use in response planning. We call this mismatch the…

• MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation

  Ke Wang, Houxing Ren, Zimu Lu, Yunqiao Yang, Zhuofan Zong, Mingjie Zhan, Hongsheng Li · 30. September 2026

  End-to-end full-duplex speech models have brought open-source machine conversation closer to human-like interaction, yet existing systems remain limited in two intertwined dimensions: long-context robustness and multi-party interaction. Real-world scenarios such as meetings, group lessons, and socia…

• When Users Change Their Minds: Measuring and Repairing Intent Drift in LLM Agents

  Yanjie Zhang, Bowen Cao, Zixin Chen, Yushi Sun · 29. September 2026

  LLM agents often operate over multi-turn interactions in which user intent changes before execution. We study intent drift: the failure mode in which superseded parts of the user's intent continue to influence the final answer or tool action. We introduce IntentFlux, an executable benchmark that con…

• Communication between Frozen Large Language Models via Prompt Optimization in a Referential Game

  Vivek Anand, Muthu Chandrasekaran, Shiva Chaitanya · 29. September 2026

  We study communication between two frozen large language models from different providers, with different tokenizers, accessed through their API endpoints. The two play a referential game: one sees an object and describes it in a short fixed-length message over a small alphabet; the other must pick t…

• DuplexCadence: Exact State and Execution from a Speech Model's Declared Timelines

  Haixiao Gao, Yimin Zheng, Linyou Xiao, Zeke Xie · 29. September 2026

  Full-duplex speech models support streaming interaction that listens and speaks at the same time. Serving them is governed by a strict, repeating deadline: conversation advances on a one-second cadence, and every second of input must be turned into a second of speech before the next second arrives. …

• APEX-Voice: Can Voice Agents Complete Professional Workflows Through Full-Duplex Interaction

  Puneet Mathur, Dinesh Manocha · 29. September 2026

  Full-duplex voice agents can now listen, speak, use tools, and act during spoken interactions, but fluent dialogue does not guarantee correct completion of delegated professional workflows. We introduce APEX-Voice, a benchmark of 120 interactive professional workflows spanning ten work archetypes su…