scholarly journals UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021

2021 ◽  
Author(s):  
Xinhui Chen ◽  
You Zhang ◽  
Ge Zhu ◽  
Zhiyao Duan
Keyword(s):  
Detection System ◽  
Synthetic Speech ◽  
Speech Detection

scholarly journals Synthetic speech detection through short-term and long-term prediction traces

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Clara Borrelli ◽  
Paolo Bestagini ◽  
Fabio Antonacci ◽  
Augusto Sarti ◽  
Stefano Tubaro
Keyword(s):  
Deep Learning ◽  
Speech Processing ◽  
Synthetic Speech ◽  
Opinion Formation ◽  
Closed Set ◽  
Speech Detection ◽  
Open Set ◽  
Technological Advances ◽  
Speech Generation ◽  
Long Term Prediction

AbstractSeveral methods for synthetic audio speech generation have been developed in the literature through the years. With the great technological advances brought by deep learning, many novel synthetic speech techniques achieving incredible realistic results have been recently proposed. As these methods generate convincing fake human voices, they can be used in a malicious way to negatively impact on today’s society (e.g., people impersonation, fake news spreading, opinion formation). For this reason, the ability of detecting whether a speech recording is synthetic or pristine is becoming an urgent necessity. In this work, we develop a synthetic speech detector. This takes as input an audio recording, extracts a series of hand-crafted features motivated by the speech-processing literature, and classify them in either closed-set or open-set. The proposed detector is validated on a publicly available dataset consisting of 17 synthetic speech generation algorithms ranging from old fashioned vocoders to modern deep learning solutions. Results show that the proposed method outperforms recently proposed detectors in the forensics literature.

A cross-vocoder study of speaker independent synthetic speech detection using phase information

2014 ◽  
Author(s):  
Jon Sanchez ◽  
Ibon Saratxaga ◽  
Inma Hernaez ◽  
Eva Navas ◽  
Daniel Erro
Keyword(s):  
Synthetic Speech ◽  
Phase Information ◽  
Speech Detection ◽  
Speaker Independent

Significance of Subband Features for Synthetic Speech Detection

2020 ◽  
Vol 15 ◽  
pp. 2160-2170 ◽  
Author(s):  
Jichen Yang ◽  
Rohan Kumar Das ◽  
Haizhou Li
Keyword(s):  
Synthetic Speech ◽  
Speech Detection

scholarly journals Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Chi-Te Wang ◽  
Ji-Yan Han ◽  
Shih-Hau Fang ◽  
Ying-Hui Lai
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Background Noise ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Detection Accuracy ◽  
Real Time Processing ◽  
Speech Detection ◽  
Reduction Function

BACKGROUND Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. OBJECTIVE This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. METHODS We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. RESULTS The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. CONCLUSIONS This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.

scholarly journals A comparison of features for synthetic speech detection

2015 ◽  
Author(s):  
Md. Sahidullah ◽  
Tomi Kinnunen ◽  
Cemal Hanilçi
Keyword(s):  
Synthetic Speech ◽  
Speech Detection

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 69
Author(s):  
Wassen Aldjanabi ◽  
Abdelghani Dahou ◽  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed Mohamed Helmi ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Detection System ◽  
Language Model ◽  
Arabic Language ◽  
Social Phenomena ◽  
Speech Detection ◽  
Task Learning ◽  
Opinion Expression ◽  
Social Media Platforms

As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

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