Phone level localization of mis-articulation is a key requirement for an automatic articulation error assessment system. A robust phone segmentation technique is essential to aid in real-time assessment of phone level mis-articulations of speech, wherein the audio is recorded on mobile phones or tablets. This is a non-standard recording set-up with little control over the quality of recording. We propose a novel post processing technique to aid Spectral Transition Measure(STM)-based phone segmentation under noisy conditions such as environment noise and clipping, commonly present during a mobile phone recording. A comparison of the performance of our approach and phone segmentation using traditional MFCC and PLPCC speech features for Gaussian noise and clipping is shown. The proposed approach was validated on TIMIT and Hindi speech corpus and was used to compute phone boundaries for a set of speech, recorded simultaneously on three devices - a laptop, a stationarily placed tablet and a handheld mobile phone, to simulate different audio qualities in a real-time non-standard recording environment. F-ratio was the metric used to compute the accuracy in phone boundary marking. Experimental results show an improvement of 7% for TIMIT and 10% for Hindi data over the baseline approach. Similar results were seen for the set of three of recordings collected in-house.
Investigating differences in lab-quality and remote recording methods with dynamic acoustic measures
Newcastle University（纽卡斯尔大学）；Institute of Phonetics and Speech Processing, Ludwig-Maximilians-Universität Munich（慕尼黑路德维希-马克西米利安大学语音学与言语处理研究所）；Eberhard Karls Universität Tübingen（图宾根埃伯哈德·卡尔斯大学）
Improving Voice Separation by Incorporating End-to-end Speech Recognition
Sony Corporation（索尼公司）；University of Tsukuba（筑波大学）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Indian Institute of Science（印度科学学院）；Sony India Software Centre（索尼印度软件中心）
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings
Fraunhofer Institute for Digital Media Technology（弗劳恩霍夫数字媒体技术研究所）；Fraunhofer Institute for Intelligent Analysis and Information Systems（弗劳恩霍夫智能分析与信息系统研究所）
The Second DIHARD Diarization Challenge: Dataset, task, and baselines
University of Pennsylvania（宾夕法尼亚大学）；Linguistic Data Consortium（语言数据联盟）；Baidu Research（百度研究院）；PSL University（巴黎文理研究大学）；ENS（巴黎高等师范学院）；EHESS（法国高等社会科学研究院）；CNRS（法国国家科学研究中心）；University of Science and Technology of China（中国科学技术大学）；Indian Institute of Science（印度科学学院）
Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration
Southeast University（东南大学）；Emory University School of Medicine（埃默里大学医学院）；Georgia Institute of Technology（佐治亚理工学院）
目的：提出了一种基于时间成帧自适应网络（TFAN）的心音分割算法，该算法包括状态转移丢失和动态推断，用于解码最可能的状态序列。研究方法：与以前的最先进的方法相比，基于TFAN的方法不需要任何关于心音状态持续时间的知识，因此可能推广到非窦性心律。基于TFAN的方法在2016年PhysioNet/Computer in Cardiology Challenge的训练集A中随机选择的50个记录上进行训练，并在其他12个独立的训练和测试数据库（2099个记录和52180次心跳）上进行测试。将用于细分的数据库分为三个难度递增的等级（第一、第二和第三等级），用于性能报告。结果如下：基于TFAN的方法在所有12个数据库中获得了优异的F1分数，除了“测试B”，平均为96.7%，而最先进的方法为94.6%。此外，基于TFAN的方法在LEVEL-I、-II和-III数据上的总体F1评分分别为99.2%、94.4%和91.4%，而当前最先进的方法分别为98.4%、88.54%和79.80%。结论：因此，基于TFAN的方法提供了实质性的改进，特别是对于更困难的情况，以及在公共训练数据中未表示的数据集。意义：所提出的方法是高度灵活的，可能适用于其他非平稳时间序列。需要进一步的工作来理解这种方法将在多大程度上提供改进的诊断性能，尽管假设更好的分割将导致改进的诊断是合乎逻辑的。
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings
Avignon University（阿维尼翁大学）；Bogazici University（海峡大学）；Brno University of Technology（布尔诺理工大学）；LISN CNRS（法国国家科学研究中心LISN实验室）；University of Sheffield（谢菲尔德大学）；Naver Labs Europe（Naver欧洲实验室）；University Grenoble Alpes（格勒诺布尔阿尔卑斯大学）
MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing
University of California, San Francisco（加州大学旧金山分校）；New York University（纽约大学）；NYU Abu Dhabi Institute（纽约大学阿布扎比研究所）；Ernst Struengmann Institute for Neuroscience（恩斯特·施特伦曼神经科学研究所）；PSL University（巴黎文理研究大学）
Statistical Context-Dependent Units Boundary Correction for Corpus-based Unit-Selection Text-to-Speech
Università di Pisa（比萨大学）；Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale di Ricerca（国家研究委员会认知科学与技术研究所）；University of Sheffield（谢菲尔德大学）
Learning Marmoset Vocal Patterns with a Masked Autoencoder for Robust Call Segmentation, Classification, and Caller Identification
RIKEN Center for Advanced Intelligence Project (AIP), Japan（日本先进人工智能项目研究所）；Keio University, Japan（庆应大学）；Nara Institute of Science and Technology, Japan（奈良科学技術大学）；The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））
Assessing clinical utility of Machine Learning and Artificial Intelligence approaches to analyze speech recordings in Multiple Sclerosis: A Pilot Study
Charles University（查理大学）；Czech Technical University in Prague（布拉格捷克理工大学）；General University Hospital（综合大学医院）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
