About this Research Topic
In today's scenario, human-machine interaction is considered advanced due to the employment of voice assistance technologies and natural language processing to aid humans with various activities. Furthermore, with the enhancement of speech recognition technology and voice translation systems, several benefits might be realized in daily activities, especially for physically challenged individuals. The automated systems might create acoustic speech signals via intelligent human-machine interaction technologies. Similarly, the interaction might be conducted with the voice as an input and digital signal outputs due to speech processing and analysis and speech-to-text conversions. Likewise, with the deployment of Android systems, microcontrollers, Bluetooth systems, and MATLAB and computer platforms, human-machine interaction systems for everyday tasks might be deployed.
N-grams, artificial neural networks, dynamic temporal wrapping systems, and hidden Markov process models all contribute to the efficiency of voice recognition algorithms. The voice recognition patterns might be processed successfully with NLP and deep learning models. Along with the aforementioned technologies, neural networks, signal processing, fuzzy models, and pattern recognition systems may be employed to perform successful speech analysis and intelligent human-computer interactions.
Through human-machine interaction, some of the voice inputs sent to computer systems for analysis may be handled automatically through speech recognition systems. The integration, as mentioned above, is often used in the construction of robotic systems, the control of digital devices, the assistance of people with visual and hearing impairments, and hands-free communication. The applications might be employed in various disciplines, including medicine, driverless automated cars, smart device controls, voice dialing, and assistive devices with automated speech control.
Human-machine interaction and social intelligence systems both include AI algorithms. Moreover, the process of responding rationally in response to the environment may be accomplished via the use of social intelligence systems, human-machine interaction, and automated voice recognition systems. The finest illustration is the deployment of humanoid robots that combine the ability to behave rationally with automatic voice recognition and the ability to communicate with people in a specific context.
This Research Topic explores how automated speech synthesis can be implemented using AI algorithms with social, intelligent human-machine interaction systems. Thus, researchers and academics may include various cognitive systems that help develop human-machine interaction using voice synthesis and analysis technologies.
Research topics could include but are not restricted to:
● Deployment of hidden Markov models for speech synthesis along with HCI
● Automated speech recognition and HCI for home automation and intelligent humanoid system design
● Augmented communication networks for the deployment of HCI in socially intelligent systems
● Multimodal interaction systems for the deployment of speech recognition systems in HCI environment
● Rational human behavioral analysis systems for speech recognition enabled HCI
● HCI in integration with automated speech recognition systems for the deployment of assistive systems for hearing and visually impaired persons
● Effective digital control devices with automated speech recognition and HCI
● Optimized hands-free technology for enabling speech recognition and HCI in PDA
● Noised speech evaluation and speech analysis with HCI optimization
● NLP based speech processing and recognition methods with effective HCI
N-grams, artificial neural networks, dynamic temporal wrapping systems, and hidden Markov process models all contribute to the efficiency of voice recognition algorithms. The voice recognition patterns might be processed successfully with NLP and deep learning models. Along with the aforementioned technologies, neural networks, signal processing, fuzzy models, and pattern recognition systems may be employed to perform successful speech analysis and intelligent human-computer interactions.
Through human-machine interaction, some of the voice inputs sent to computer systems for analysis may be handled automatically through speech recognition systems. The integration, as mentioned above, is often used in the construction of robotic systems, the control of digital devices, the assistance of people with visual and hearing impairments, and hands-free communication. The applications might be employed in various disciplines, including medicine, driverless automated cars, smart device controls, voice dialing, and assistive devices with automated speech control.
Human-machine interaction and social intelligence systems both include AI algorithms. Moreover, the process of responding rationally in response to the environment may be accomplished via the use of social intelligence systems, human-machine interaction, and automated voice recognition systems. The finest illustration is the deployment of humanoid robots that combine the ability to behave rationally with automatic voice recognition and the ability to communicate with people in a specific context.
This Research Topic explores how automated speech synthesis can be implemented using AI algorithms with social, intelligent human-machine interaction systems. Thus, researchers and academics may include various cognitive systems that help develop human-machine interaction using voice synthesis and analysis technologies.
Research topics could include but are not restricted to:
● Deployment of hidden Markov models for speech synthesis along with HCI
● Automated speech recognition and HCI for home automation and intelligent humanoid system design
● Augmented communication networks for the deployment of HCI in socially intelligent systems
● Multimodal interaction systems for the deployment of speech recognition systems in HCI environment
● Rational human behavioral analysis systems for speech recognition enabled HCI
● HCI in integration with automated speech recognition systems for the deployment of assistive systems for hearing and visually impaired persons
● Effective digital control devices with automated speech recognition and HCI
● Optimized hands-free technology for enabling speech recognition and HCI in PDA
● Noised speech evaluation and speech analysis with HCI optimization
● NLP based speech processing and recognition methods with effective HCI
Keywords: Human-machine interaction, Automated speech recognition, Artificial intelligence, Socially intelligent systems, Natural language processing
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