The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our study applies a modified Transformer in a speech enhancement task. Specifically, positional encoding in the Transformer may not be necessary for speech enhancement, and hence, it is replaced by convolutional layers. To further improve the perceptual evaluation of the speech quality (PESQ) scores of enhanced speech, the L_1 pre-trained Transformer is fine-tuned using a MetricGAN framework. The proposed MetricGAN can be treated as a general post-processing module to further boost the objective scores of interest. The experiments were conducted using the data sets provided by the organizer of the Deep Noise Suppression (DNS) challenge. Experimental results demonstrated that the proposed system outperformed the challenge baseline, in both subjective and objective evaluations, with a large margin.
MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；The Ohio State University（俄亥俄州立大学）；Mila-Quebec AI Institute（米拉-魁北克人工智能研究所）；National Institute of Information and Communications Technology（日本信息通信研究机构）
MetricGAN+/-: Increasing Robustness of Noise Reduction on Unseen Data
The University of Sheffield（谢菲尔德大学）
语音增强系统的训练通常不包含人类感知的知识，因此可能导致不自然的声音结果。通过预测器网络将心理声学激励的语音感知度量作为模型训练的一部分最近引起了人们的兴趣。然而，这种预测器的性能受到出现在训练数据中的度量分数的分布的限制。在这项工作中，我们提出了MetricGAN+/-（MetricGAN+的扩展，一个这样的度量激励系统），它引入了一个额外的网络-一个“去生成器”，试图通过确保在训练中观察更广泛的度量分数来提高预测网络的鲁棒性（并通过扩展生成器）。在VoiceBank-DEMAND数据集上的实验结果显示，PESQ评分相对提高了3.8%（3.05 vs 3.22 PESQ评分），并且对看不见的噪声和语音有更好的泛化能力。
RDSE: Real-Time Directional Speech Enhancement Framework for Dual-Mic Commodity Smartphones
National Institute of Technology Jalandhar（贾朗达尔国家技术学院）；Kalinga Institute of Industrial Technology（卡林加工业技术学院）；IMC University of Applied Sciences（IMC应用科技大学）
Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch Boosting
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Cadre Division Organization Department of the Party Committee, Xinjiang Uygur Autonomous Region（中国新疆维吾尔自治区党委组织部干部处）
Exploring the Potential of Data-Driven Spatial Audio Enhancement Using a Single-Channel Model
Faculdade de Engenharia Elétrica e de Computação（电气与计算工程学院）；Universidade Estadual de Campinas（坎皮纳斯州立大学）；Faculdade de Engenharia Acústica（声学工程学院）；Universidade Federal de Santa Maria（圣玛丽亚联邦大学）
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Forestry University（北京林业大学）
Boosting keyword spotting through on-device learnable user speech characteristics
Integrated Systems Laboratory (IIS), ETH Zurich（苏黎世联邦理工学院集成系统实验室）；Zurich Research Center, Huawei Technologies（华为技术有限公司苏黎世研究中心）；DEI, University of Bologna（博洛尼亚大学电子、计算机与工程系）
Unveiling Hidden Factors: Explainable AI for Feature Boosting in Speech Emotion Recognition
University of Québec (TÉLUQ)（魁北克大学（TÉLUQ））；Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering（康考迪亚信息系统工程学院）；University of Québec at Montréal（蒙特利尔魁北克大学）；Centre for Research and Intervention on Suicide, Ethical Issues and End-of-Life Practices（自杀、伦理问题与临终实践研究与干预中心）
Boosting the Transferability of Audio Adversarial Examples with Acoustic Representation Optimization
National Engineering Research Center of Disaster Backup and Recovery（国家防灾备份与恢复工程研究中心）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）
Boosting Cross-Domain Speech Recognition with Self-Supervision
Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft Research Asia（微软亚洲研究院）；Microsoft STCA（微软（亚洲）互联网工程院）
