Recognition of Voice Commands Based on Neural Network

Lyashenko, Vyacheslav; Laariedh, Farah; Sotnik, Svitlana; Moheb, Ahmad

doi:10.18421/tem102-13

Cited by 9 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep Learning models, such as CNNs, have exhibited remarkable results in improving the accuracy of voice recognition systems. These networks excel at extracting meaningful features from audio data, thereby enhancing recognition performance and enabling the system to adapt and recognize diverse speakers more effectively [42].…”

Section: Voice Recognitionmentioning

confidence: 99%

Integration of Deep Learning and Collaborative Robot for Assembly Tasks

Mendez,

Ochoa,

Olivera-Guzman

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Human–robot collaboration has gained attention in the field of manufacturing and assembly tasks, necessitating the development of adaptable and user-friendly forms of interaction. To address this demand, collaborative robots (cobots) have emerged as a viable solution. Deep Learning has played a pivotal role in enhancing robot capabilities and facilitating their perception and understanding of the environment. This study proposes the integration of cobots and Deep Learning to assist users in assembly tasks such as part handover and storage. The proposed system includes an object classification system to categorize and store assembly elements, a voice recognition system to classify user commands, and a hand-tracking system for close interaction. Tests were conducted for each isolated system and for the complete application as used by different individuals, yielding an average accuracy of 91.25%. The integration of Deep Learning into cobot applications has significant potential for transforming industries, including manufacturing, healthcare, and assistive technologies. This work serves as a proof of concept for the use of several neural networks and a cobot in a collaborative task, demonstrating communication between the systems and proposing an evaluation approach for individual and integrated systems.

show abstract

Section: Voice Recognitionmentioning

confidence: 99%

Integration of Deep Learning and Collaborative Robot for Assembly Tasks

Mendez,

Ochoa,

Olivera-Guzman

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, Arabic command recognition is a complex task and requires a significant amount of computational and linguistic resources [5]. Recognition accuracy can vary depending on factors such as the quality of the audio input, the size and quality of the language model, and the complexity of the commands being recognized [6]. Ongoing research and advancements in natural language processing and speech recognition technologies are continually improving the accuracy and performance of Arabic command recognition systems.…”

Section: Introductionmentioning

confidence: 99%

ArabAlg: A new Dataset for Arabic Speech Command Recognition for Machine Learning Applications

OUKAS,

HABOUSSI,

MAIZA

et al. 2024

IJCDS

View full text Add to dashboard Cite

Automatic Speech Recognition (ASR) systems have witnessed significant advancements in recent years, thanks to the emergence of deep learning techniques and the availability of large speech datasets in various languages. With the increasing demand for Arabic voice-enabled technologies, the availability of a high-quality and representative dataset for the Arabic language becomes crucial. This paper presents the development of a new dataset called ArabAlg, specifically designed for Arabic Speech Command Recognition (ASCR), to support the integration of Arabic voice recognition systems into smart devices in the Internet of Things (IoT). This research focuses on collecting and annotating a diverse range of Arabic speech commands, encompassing various domains and applications. The dataset construction process involves recording and preprocessing several utterances from native Arabic speakers. To ensure precision and reliability, quality control measures are implemented during data collection and annotation. The resulting dataset provides a valuable resource for training and evaluating ASCR systems tailored for Arabic speakers using Machine Learning and Deep Learning.

show abstract

“…Currently, robotics is very actively developing [1][2][3][4][5][6]. Aviation, household, military, space, medical, underwater, industrial, and construction robotics exist.…”

Section: Introductionmentioning

confidence: 99%

Access Control to Robotic Systems Based on Biometric: The Generalized Model and its Practical Implementation

2023

IJIES

View full text Add to dashboard Cite

The paper presents a generalized system's structural scheme and components for monitoring and controlling access to robotic systems based on biometric data for decision-making. Mathematical decision-making models for choosing the best alternative based on fuzzy sets are proposed. The fuzzy analytical hierarchy process determined weights of 6 criteria (for k1 criterion weighting factor is equal to 2.2; for k2 criterion -1.8; for k3 criterion -0.6; for k4 criterion -0.6; for k5 criterion -0.6; for k6 criterion -0.2, that is, most weighty would be k1recognition accuracy) against which the best option was evaluated. Thus, during evaluation of alternative on six criteria it was obtained that t alternative X1 (face recognition in full-face), will have the key value of membership function of resulting fuzzy set alternatives with ideal value of integral criterion, equal to 0.53, and as result, implemented rational choice, taking into account given criteria. The difference is that we have introduced such criteria as anti-spoofing. The access control system for robotic systems works in real-time. It is implemented based on an algorithmic decision-making complex, which includes two-factor authentication to increase security: registration involves entering employee data from the keyboard (password, login, surname, first name, position) and physiological authentication (identification by face). Face detection is proposed to be implemented on several images: face, profile, face, and profile in the mask. A distinctive feature of our development is that often, a main requirement for "access control systems with facial identification" is the frontal location of the face relative to the camera; in our case, the image of the face at an angle was added.

show abstract

Recognition of Voice Commands Based on Neural Network

Cited by 9 publications

References 19 publications

Integration of Deep Learning and Collaborative Robot for Assembly Tasks

Integration of Deep Learning and Collaborative Robot for Assembly Tasks

ArabAlg: A new Dataset for Arabic Speech Command Recognition for Machine Learning Applications

Access Control to Robotic Systems Based on Biometric: The Generalized Model and its Practical Implementation

Contact Info

Product

Resources

About