Textual escalation detection has been widely applied to e-commerce companies' customer service systems to pre-alert and prevent potential conflicts. Similarly, in public areas such as airports and train stations, where many impersonal conversations frequently take place, acoustic-based escalation detection systems are also useful to enhance passengers' safety and maintain public order. To this end, we introduce a system based on acoustic-lexical features to detect escalation from speech, Voice Activity Detection (VAD) and label smoothing are adopted to further enhance the performance in our experiments. Considering a small set of training and development data, we also employ transfer learning on several wellknown emotional detection datasets, i.e. RAVDESS, CREMA-D, to learn advanced emotional representations that is then applied to the conversational escalation detection task. On the development set, our proposed system achieves 81.5% unweighted average recall (UAR) which significantly outperforms the baseline with 72.2% UAR.
Lexical Access Model for Italian -- Modeling human speech processing: identification of words in running speech toward lexical access based on the detection of landmarks and other acoustic cues to features
Massachusetts Institute of Technology (MIT)（麻省理工学院）；Sapienza University of Rome（罗马大学）；Radcliffe Institute for Advanced Study, Harvard University（哈佛大学拉德克利夫高级研究所）
为了模拟听者在推导出说话者想要表达的词语时所激发的过程，需要对词汇项如何存储在记忆中设定一个假设。这项工作的目的是开发一个系统，模仿人类识别运行语音中的单词，并以这种方式，提供一个框架，以更好地理解人类的语音处理。我们建立了一个语音识别器为意大利语的基础上史蒂文斯的词汇访问模型的原则，其中单词存储为层次安排的区别性特征（史蒂文斯，K。n.（2002年）的报告。"Toward a model for lexical access based on acoustic landmarks and distinct features，" J. Acoust. Soc. Am.，111（4）：1872 - 1891）。在过去的几十年里，麻省理工学院（MIT）的语音通信组基于这种方法开发了一个英语语音识别系统。意大利语将是英语之外的第一种语言;扩展到另一种语言提供了一个机会来测试假设，即单词在记忆中表示为一组分层排列的独特特征，并揭示哪些潜在机制可能具有语言独立性。本文还介绍了一个新的词汇访问语料库，LaMIT数据库，创建和标记专门为这项工作，将免费提供给语音研究社区。未来的发展将测试这一假设，即特定的声学不连续性-称为地标-作为特征的线索，是语言独立的，而其他线索可能是语言依赖的，这对理解人类大脑如何识别语音具有重要意义。
Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data
Large Language Models for Dysfluency Detection in Stuttered Speech
Technische Hochschule Nürnberg Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆应用技术大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Intel Labs（英特尔实验室）
本文将多标签口吃不流利检测建模为语言建模问题，向 LLM 输入 ASR 假设候选与音频编码器声学表征并微调，在英语、德语数据集上取得有竞争力的结果。
CLASH: Counterfactual Auditing of Lexical and Prosodic Reliance in Spoken Sarcasm Detection
Imperial College London（伦敦帝国理工学院）；New York University（纽约大学）；Technical University of Munich（慕尼黑工业大学）；Tencent Inc.（腾讯公司）；Wuhan University（武汉大学）；Nankai University（南开大学）
CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonese
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Hong Kong University of Science and Technology（香港科学与技术大学）；National Taiwan University, Taiwan（台湾国立台湾大学）；Columbia University（哥伦比亚大学）；WeBank Co., Ltd., Shenzhen, China（深圳网商银行有限公司）
Multi-modal Automated Speech Scoring using Attention Fusion
Indraprastha Institute of Information Technology（英德拉普拉斯塔信息技术学院）；Netaji Subhas Institute of Technology（内塔吉·苏巴斯理工学院）；Teachers College, Columbia University（哥伦比亚大学教师学院）；Second Language Testing Inc.（第二语言测试公司）
Motion-Aware Reasoning from Speech to Mask Tracks: Runner-up Solution for the MeViS-Audio Track of the 8th LSVOS Challenge 2026
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Anhui University of Science and Technology（安徽理工大学）；National University of Singapore（新加坡国立大学）；China Agricultural University（中国农业大学）
Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model
Faculty of Information Technology, University of Jyväskylä（于韦斯屈莱大学信息技术学院）；Faculty of Sport and Health Sciences, University of Jyväskylä（于韦斯屈莱大学体育与健康科学学院）
The NVVSpeech Challenge 2026: Overview and Lessons for Speech Understanding and Generation with Nonverbal Vocalizations
School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Shenzhen Pimei Technology（深圳湃美科技）；National Taiwan University（台湾大学）；Tongji University（同济大学）；Hong Kong University of Science and Technology（香港科技大学）；Yutuzhineng（语知能）；AISHELL；State Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
