Validation of Voice Recognition in Various Google Voice Languages using Voice Recognition Module V3 Based on Microcontroller

Khotimah, Khusnul; Santoso, Agus; Ma’arif, M.; Azhiimah, Alfiantin Noor; Suprianto, Bambang; Sumbawati, Meini Sondang; Rijanto, Tri

doi:10.1109/icvee50212.2020.9243184

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…The ESPduino-32 board can be used to facilitate the provision of an Arduino that has been equipped with an ESP-32 module for Wi-Fi connection [6]. Voice Recognition Module v3.1 can detect and change voices in certain activities [7]. Approximately all those devices will cost around 30-40 USD.…”

Section: Voice-based Chatbot Developmentmentioning

confidence: 99%

Arduino, Raspberry Pi, and Smartphone Usage Comparison for Voice-based Virtual Assistant

Sanjaya

Wibhowo

2022

2022 6th International Conference on Information Technology (InCIT)

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A virtual assistant or an artificial intelligence chatbot is one of the technological interventions to help individuals with borderline personality disorder (BPD).The virtual assistant must have a bank of knowledge to answer the questions from individuals with BPD to accompany and chat with them like a digital friend. As part of artificial intelligence software, Program-O can handle the interaction between users and the bank of knowledge in the server. The voice recognition and textto-speech features can be used to enhance the virtual assistant's function to become even closer as a friend. There are three possibilities for implementing voice recognition and text-to-speech features on virtual assistants, which are using Arduino, Raspberry Pi, and smartphone devices. All these three devices can be connected to the speech recognition module or Application Protocol Interface (API) for voice recognition on the internet. This paper will explore and compare several aspects of the implementation of voice recognition and text-to-speech features in the virtual assistant. The result will be a recommendation for the application of voice recognition and text-to-speech features on a virtual assistant for individuals with BPD.

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Section: Voice-based Chatbot Developmentmentioning

confidence: 99%

Arduino, Raspberry Pi, and Smartphone Usage Comparison for Voice-based Virtual Assistant

Sanjaya

Wibhowo

2022

2022 6th International Conference on Information Technology (InCIT)

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“…Voice recognition identifies people using their unique voice acoustic features, matched with the copy of the digital template stored in the database (Thomas et al, 2020;Khotimah et al, 2020;Ye & Yang, 2021). Voice recognition determines the person's identity based on the voice, not the content of the speech (Kandhari et al, 2018;Al-Tekreeti & Ibrahim, 2020).…”

Section: (V) Voice Recognitionmentioning

confidence: 99%

“…Voice recognition is applied in various fields, including telebanking, video games, household appliances, surveillance and security systems, mobile application, voice dialling, online services, personal assistant services, and access control (Khotimah et al, 2020;Thomas et al, 2020;Krčadinac et al, 2021;Ye & Yang, 2021). Voice recognition is also applied in mobile money systems.…”

Section: (V) Voice Recognitionmentioning

confidence: 99%

“…Voice recognition offers several benefits, such as the uniqueness of the human voice, security in access control, not requiring new hardware, and good compatibility (Thomas et al, 2020;Khotimah et al, 2020). Nevertheless, voice recognition is sensitive to background noise, harsh environments, playback spoofing, emotional states, voice changes over time, characteristics of the microphones used, accent differences, and quality of the communication channel during enrolment and authentication (Krčadinac et al, 2021;Cayir & Navruz, 2021).…”

Section: (V) Voice Recognitionmentioning

confidence: 99%

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Development of a secure multi-factor authentication algorithm for mobile money applications

Guma¹

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With the evolution of industry 4.0, financial technologies have become paramount and mobile money as one of the financial technologies has immensely contributed to improving financial inclusion among the unbanked population. Several mobile money schemes were developed but, they suffered severe authentication security challenges since they implemented two-factor authentication. This study focused on developing a secure multi-factor authentication (MFA) algorithm for mobile money applications. It uses personal identification numbers, one-time passwords, biometric fingerprints, and quick response codes to authenticate and authorize mobile money subscribers. Secure hash algorithm-256, Rivest-Shamir-Adleman encryption, and Fernet encryption were used to secure the authentication factors, confidential financial information and data before transmission to the remote databases. A literature review, survey, evolutionary prototyping model, and heuristic evaluation and usability testing methods were used to identify authentication issues, develop prototypes of native genuine mobile money (G-MoMo) applications, and identify usability issues with the interface designs and ascertain their usability, respectively. The results of the review grouped the threat models into attacks against privacy, authentication, confidentiality, integrity, and availability. The survey identified authentication attacks, identity theft, phishing attacks, and PIN sharing as the key mobile money systems’ security issues. The researcher designed a secure MFA algorithm for mobile money applications and developed three native G-MoMo applications to implement the designed algorithm to prove the feasibility of the algorithm and that it provided robust security. The algorithm was resilient to non-repudiation, ensured strong authentication security, data confidentiality, integrity, privacy, and user anonymity, was highly effective against several attacks but had high communication overhead and computational costs. Nevertheless, the heuristic evaluation results showed that the G-MoMo applications’ interface designs lacked forward navigation buttons, uniformity in the applications’ menu titles, search fields, actions needed for recovery, and help and documentation. Similarly, the usability testing revealed that they were easy to learn, effective, efficient, memorable, with few errors, subscriber satisfaction, easy to use, aesthetic, easy to integrate, and understandable. Implementing a secure mobile money authentication and authorisation by combining multiple factors which are securely stored helps mobile money subscribers and other stakeholders to have trust in the developed native G-MoMo applications.

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Section: (A) Voice Recognitionmentioning

confidence: 99%

Development of a secure multi-factor authentication algorithm for mobile money applications

Ali

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With the expansion of industry 4.0, financial technology (FinTech) has become paramount in this era. Mobile money as one of the FinTech has immensely contributed to improving financial inclusions among the unbanked population in many developing countries. Several mobile money schemes were developed to ensure easy access to mobile money services. However, they have suffered severe authentication security challenges since implementing two-factor authentication (2FA). Therefore, this research developed a secure multi-factor authentication (MFA) algorithm for mobile money applications that combines personal identification number (PIN), one-time password (OTP), and biometric fingerprints to authenticate the mobile money subscribers. It also used the customer’s biometric fingerprints and the agent’s quick response (QR) code to authorise money withdrawal. The PINs and OTP are secured by secure hashing algorithm-256 (SHA-256) and biometric fingerprints by Fast IDentity Online (FIDO), where the Rivest-Shamir-Adleman (RSA) encryption protects the public/private key pair and the fingerprint templates. The QR codes, confidential financial information in the databases, and all the data before transmission to the remote databases are secured using Fernet encryption. A design science research approach was employed in the research using a mixed-method. The review results identified and grouped the threat models into attacks against privacy, authentication, confidentiality, integrity, and availability. The cryptographic functions and personal identification were the countermeasures. The survey identified authentication attacks, identity theft, phishing attacks, and PIN sharing as the crucial security issues Uganda’s mobile money systems encountered. The security analysis of the designed algorithm and developed native genuine mobile money (G-MoMo) applications proved that it provided robust security during authentication and ensured data confidentiality, integrity, privacy and user anonymity. It is highly effective against several security attacks and resilient to non-repudiation. The performance analysis results showed that the algorithm enhanced security but had high communication overhead and computational cost. Lack of a forward navigation button, lack of uniformity in the applications menu title, lack of search field options, lack of actions needed for recovery, and lack of help & documentation, were identified as the results of the usability issues with the native G-MoMo applications’ user interfaces. While the results of the usability testing showed that the native G-MoMo applications were learnable, effective, efficient, memorable, had few errors, satisfaction, ease of use, aesthetic, helpful, easy to integrate, and understandable. In conclusion, implementing a secure mobile money authentication using the ii novel approach combining multiple factors helps mobile money subscribers and other stakeholders trust the mobile money industry since the security goals are highly maintained.

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Validation of Voice Recognition in Various Google Voice Languages using Voice Recognition Module V3 Based on Microcontroller

Cited by 13 publications

References 7 publications

Arduino, Raspberry Pi, and Smartphone Usage Comparison for Voice-based Virtual Assistant

Arduino, Raspberry Pi, and Smartphone Usage Comparison for Voice-based Virtual Assistant

Development of a secure multi-factor authentication algorithm for mobile money applications

Development of a secure multi-factor authentication algorithm for mobile money applications

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