SPEECH COMMUNICATION 是一本跨学科期刊，其主要目标是满足快速传播和深入讨论基础和应用研究成果的需求。该期刊的主要目标是：• 为人类和人机技术的进步提供一个论坛语音通信科学；• 促进该领域不同领域之间的交流；• 促进该领域科学合理贡献的快速和广泛传播。

Speech Communication is an interdisciplinary journal aimed at meeting the needs for rapid dissemination and in-depth discussion of fundamental and applied research findings. The main objectives of the journal are: to provide a forum for the advancement of speech communication science for both humans and human-machine technologies; to facilitate communication across different disciplines within the field; and to promote the rapid and widespread dissemination of scientifically sound contributions in the field.

《Speech Communication》是一本跨学科的期刊，旨在满足基础和应用研究成果的快速传播与深入讨论的需求。该期刊的主要目标包括：为人类与人机技术的进步提供一个语音通信科学的论坛；促进该领域不同学科之间的交流；加速和广泛传播该领域的科学合理贡献。

Speech Communication

Yen-Chen Hao

Contextual effect in second language perception and production of Mandarin tones

第二语言中普通话声调感知与产出的语境效应

Fake audio detection is a growing concern and some relevant datasets have been designed for research. However, there is no standard public Chinese dataset under complex conditions. In this paper, we aim to fill in the gap and design a Chinese fake audio detection dataset (CFAD) for studying more generalized detection methods. Twelve mainstream speech-generation techniques are used to generate fake audio. To simulate the real-life scenarios, three noise datasets are selected for noise adding at five different signal-to-noise ratios, and six codecs are considered for audio transcoding (format conversion). CFAD dataset can be used not only for fake audio detection but also for detecting the algorithms of fake utterances for audio forensics. Baseline results are presented with analysis. The results that show fake audio detection methods with generalization remain challenging. The CFAD dataset is publicly available.1

Haoxin Ma

Jiangyan Yi

Chenglong Wang

Xinrui Yan

Jianhua Tao

Tao Wang

Shiming Wang

Ruibo Fu

CFAD: A Chinese dataset for fake audio detection

CFAD：一个用于虚假音频检测的中文数据集

Recently deep learning has been successfully used in speech recognition, however it has not been carefully explored and widely accepted for speaker verification. To incorporate deep learning into speaker verification, this paper proposes novel approaches of extracting and using features from deep learning models for text-dependent speaker verification. In contrast to the traditional short-term spectral feature, such as MFCC or PLP, in this paper, outputs from hidden layer of various deep models are employed as deep features for text-dependent speaker verification. Fours types of deep models are investigated: deep Restricted Boltzmann Machines, speech-discriminant Deep Neural Network (DNN), speaker-discriminant DNN, and multi-task joint-learned DNN. Once deep features are extracted, they may be used within either the GMM-UBM framework or the identity vector (i-vector) framework. Joint linear discriminant analysis and probabilistic linear discriminant analysis are proposed as effective back-end classifiers for identity vector based deep features. These approaches were evaluated on the RSR2015 data corpus. Experiments showed that deep feature based methods can obtain significant performance improvements compared to the traditional baselines, no matter if they are directly applied in the GMM-UBM system or utilized as identity vectors. The EER of the best system using the proposed identity vector is 0.10%, only one fifteenth of that in the GMM-UBM baseline.

Yuan Liu

Yanmin Qian

Nanxin Chen

Tianfan Fu

Ya Zhang

Kai Yu

Deep feature for text-dependent speaker verification

基于深度特征的文本相关说话人验证

Hongzhi Wang

Rachael-Anne Knight

Lucy Dipper

Roy Alderton

Reem Alyahya

Systematic Review: The Identification of Segmental Mandarin-accented English Features

系统性综述：段落性普通话口音英语特征的识别

Douglas O'Shaughnessy

Spoken Language Identification: An overview of past and present research trends

口语识别：过去与现在研究趋势概述

Kavan Fatehi

Mercedes Torres Torres

Ayse Kucukyilmaz

An overview of high-resource automatic speech recognition methods and their empirical evaluation in low-resource environments

高资源自动语音识别方法概述及其在低资源环境中的实证评估

Xinlei Ma

Ruiteng Zhang

Jianguo Wei

Xugang Lu

Junhai Xu

Lin Zhang

Wenhuan Lu

Self-distillation-based domain exploration for source speaker verification under spoofed speech from unknown voice conversion

基于自蒸馏的领域探索用于未知语音转换欺骗下的源说话人验证

Bingbing Wang

Yangjie Wei

Zhuangzhuang Wang

Zekang Qi

Improved AED with multi-stage feature extraction and fusion based on RFAConv and PSA

基于RFAConv和PSA的多阶段特征提取与融合改进AED

Abbreviation	Speech Commun.
Journal Impact	2.37
Quartiles(Global)	COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS(Q3)

ISSN	0167-6393, 1872-7182
h-index	117

Publication Information	Publisher: Elsevier B.V.，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1982，Proportion of original research papers: 98.90%，Self Citation Rate:8.30%， Gold OA Rate: 29.44%
Average review cycle	网友分享经验：较慢,6-12周来源Elsevier官网：平均19.2周
Average recruitment ratio	网友分享经验：容易来源Elsevier官网：83%

Speech Communication

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Contextual effect in second language perception and production of Mandarin tones

CFAD: A Chinese dataset for fake audio detection

Deep feature for text-dependent speaker verification

Systematic Review: The Identification of Segmental Mandarin-accented English Features

Spoken Language Identification: An overview of past and present research trends

An overview of high-resource automatic speech recognition methods and their empirical evaluation in low-resource environments

Systematic Review: The Identification of Segmental Mandarin-accented English Features

Spoken Language Identification: An overview of past and present research trends

Self-distillation-based domain exploration for source speaker verification under spoofed speech from unknown voice conversion

Improved AED with multi-stage feature extraction and fusion based on RFAConv and PSA