Attacks on Voice Assistant Systems

2022 ◽  
pp. 61-77
Author(s):  
Jie Lien ◽  
Md Abdullah Al Momin ◽  
Xu Yuan
Keyword(s):  
Speech Synthesis ◽  
Recognition System ◽  
Research Interest ◽  
Voice Conversion ◽  
Speech Recognition System ◽  
Research Attention ◽  
Replay Attacks ◽  
Assistant Systems ◽  
Authentication System ◽  
The Voice

Voice assistant systems (e.g., Siri, Alexa) have attracted wide research attention. However, such systems could receive voice information from malicious sources. Recent work has demonstrated that the voice authentication system is vulnerable to different types of attacks. The attacks are categorized into two main types: spoofing attacks and hidden voice commands. In this chapter, how to launch and defend such attacks is explored. For the spoofing attack, there are four main types, such as replay attacks, impersonation attacks, speech synthesis attacks, and voice conversion attacks. Although such attacks could be accurate on the speech recognition system, they could be easily identified by humans. Thus, the hidden voice commands have attracted a lot of research interest in recent years.

scholarly journals A Fundamental of Automatic Speech Recognition and Speech Database

2021 ◽  
Vol 9 (9) ◽  
pp. 1020-1027
Author(s):  
Sonal Anilkumar Tiwari
Keyword(s):  
Speech Recognition ◽  
Mobile Phones ◽  
Automatic Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Speech Database ◽  
The Voice

Abstract: This can be quite interesting when we think that we commanding something to in-animated objects. Yes it is possible with the help of ASR systems. Speech recognition system is a system that can make humans to talk with machineries. Nowadays speech recognition is such a technique that without it, a person cannot do any of his work properly. People get addicted of it. And it has become a habit for humans like we use mobile phones but when we want to type something, then we immediately can pass the voice commands. With which our Efforts are reduced, as well as a lot of our time. Keywords: Speech, Speech Recognition, ASR, Corpus, PRAAT

scholarly journals Beijing Opera Synthesis Based on Straight Algorithm and Deep Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
XueTing Wang ◽  
Cong Jin ◽  
Wei Zhao
Keyword(s):  
Speech Synthesis ◽  
Control Model ◽  
Voice Conversion ◽  
Chinese Opera ◽  
Wide Range ◽  
Voice Control ◽  
Music Generation ◽  
Beijing Opera ◽  
The Voice ◽  
Target Speaker

Speech synthesis is an important research content in the field of human-computer interaction and has a wide range of applications. As one of its branches, singing synthesis plays an important role. Beijing Opera is a famous traditional Chinese opera, and it is called Chinese quintessence. The singing of Beijing Opera carries some features of speech but it has its own unique pronunciation rules and rhythms which differ from ordinary speech and singing. In this paper, we propose three models for the synthesis of Beijing Opera. Firstly, the speech signals of the source speaker and the target speaker are extracted by using the straight algorithm. And then through the training of GMM, we complete the voice control model to input the voice to be converted and output the voice after the voice conversion. Finally, by modeling the fundamental frequency, duration, and frequency separately, a melodic control model is constructed using GAN to realize the synthesis of the Beijing Opera fragment. We connect the fragments and superimpose the background music to achieve the synthesis of Beijing Opera. The experimental results show that the synthesized Beijing Opera has some audibility and can basically complete the composition of Beijing Opera. We also extend our models to human-AI cooperative music generation: given a target voice of human, we can generate a Beijing Opera which is sung by a new target voice.

A Chinese Small Vocabulary Offline Speech Recognition System Based on Pocketsphinx in Android Platform

2014 ◽  
Vol 623 ◽  
pp. 267-273
Author(s):  
Xin Fei Liu ◽  
Hui Zhou
Keyword(s):  
Speech Recognition ◽  
Cell Phone ◽  
Recognition Performance ◽  
Language Model ◽  
Recognition System ◽  
Speech Recognition System ◽  
Development Environment ◽  
Online Tool ◽  
Android Development ◽  
The Voice

This paper describes a Chinese small-vocabulary offline speech recognition system based on PocketSphinx which acoustic models are regenerated by improving the existing models of Sphinx and language model is generated by LMTool online tool. And then build an offline speech recognition system which could run on the Android smartphone in Android development environment in Linux system. The experiment results show that the system used for recognizing the voice commands for cell phone has good recognition performance.

Use of an Adaptive Neuron Network in the Voice Authentication System

2006 ◽  
Vol 65 (5) ◽  
pp. 427-439
Author(s):  
O. N. Katkov ◽  
V. A. Pimenov ◽  
A. P. Ryzhkov
Keyword(s):  
Neuron Network ◽  
Authentication System ◽  
Adaptive Neuron ◽  
The Voice

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

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