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Silent speech interface

Summary

Silent speech interface is a device that allows speech communication without using the sound made when people vocalize their speech sounds. As such it is a type of electronic lip reading. It works by the computer identifying the phonemes that an individual pronounces from nonauditory sources of information about their speech movements. These are then used to recreate the speech using speech synthesis.[1]

Information sources edit

Silent speech interface systems have been created using ultrasound and optical camera input of tongue and lip movements.[2] Electromagnetic devices are another technique for tracking tongue and lip movements.[3] The detection of speech movements by electromyography of speech articulator muscles and the larynx is another technique.[4][5] Another source of information is the vocal tract resonance signals that get transmitted through bone conduction called non-audible murmurs.[6] They have also been created as a brain–computer interface using brain activity in the motor cortex obtained from intracortical microelectrodes.[7]

Uses edit

Such devices are created as aids to those unable to create the sound phonation needed for audible speech such as after laryngectomies.[8] Another use is for communication when speech is masked by background noise or distorted by self-contained breathing apparatus. A further practical use is where a need exists for silent communication, such as when privacy is required in a public place, or hands-free data silent transmission is needed during a military or security operation.[2][9]

In 2002, the Japanese company NTT DoCoMo announced it had created a silent mobile phone using electromyography and imaging of lip movement. "The spur to developing such a phone," the company said, "was ridding public places of noise," adding that, "the technology is also expected to help people who have permanently lost their voice."[10] The feasibility of using silent speech interfaces for practical communication has since then been shown.[11]

Recent Development and Research edit

Alter Ego - Arnav Kapur

Arnav Kapur, a prominent researcher from the Massachusetts Institute of Technology (MIT), has made groundbreaking strides in the field of brain-computer interfaces with his 2019 study known as AlterEgo. This remarkable innovation introduces a revolutionary concept called the "Silent Speech Interface," enabling direct communication between the human brain and external devices through subtle stimulation of the speech muscles.

Kapur's Silent Speech Interface has been hailed as a game-changer, providing individuals with a means to communicate without vocalizing their thoughts aloud. By leveraging neural signals associated with speech and language, the AlterEgo system deciphers the user's intended words and translates them into text or commands, all without the need for audible speech.

The core focus of Kapur's Research Paper lies in the development and remarkable accuracy achieved by the Silent Speech Interface. By delving deep into the intricate workings of the device, he has shed light on the complex processes that underlie its functionality, thereby offering invaluable insights into its potential applications and future advancements.

Notably, Kapur's groundbreaking research has sparked immense interest and excitement within the scientific community. The emergence of this new technology has prompted researchers from various disciplines to explore and expand upon the possibilities of brain-computer interfaces, with implications that span far beyond the realm of communication. The potential for assisting individuals with speech impairments, enabling seamless human-machine interaction, and enhancing the overall human experience has captivated the imaginations of scientists and researchers worldwide.

SpeakUP - Varun Chandrashekhar

In 2021, Varun Chandrashekhar made significant strides in the realm of Silent Speech Interface technology with his research regarding SpeakUP. Building upon the foundational work laid by Arnav Kapur's AlterEgo, Chandrashekhar's research was aimed at developing a low-cost Silent Speech Interface that could be accessible to a broader audience.

Chandrashekhar's approach was the first to involve utilizing commercially available sentences as part of the speech recognition process. By leveraging readily accessible linguistic data, he sought to create a user-friendly and cost-effective alternative to traditional voice-operated devices.

The core focus of Chandrashekhar's study revolved around identifying the most optimal signal-to-speech algorithm for these types of Silent Speech Interface devices. His meticulous research involved exploring various algorithms and methodologies to ensure the accuracy and efficiency of the system.

The significance of Chandrashekhar's work lies not only in its technological advancements but also in its potential to democratize access to Silent Speech Interfaces. By striving to make this technology more affordable and readily available, Chandrashekhar's work holds promise for empowering individuals with speech-related challenges, enabling them to communicate effectively and independently.

The emergence of SpeakUP marks another milestone in the field of brain-computer interfaces and Silent Speech technology. As this area of research continues to grow, Chandrashekhar's contribution serves as a stepping stone towards a future where seamless and intuitive human-computer communication becomes more accessible to all, ushering in a new era of assistive technology and human-machine interaction.

In fiction edit

The decoding of silent speech using a computer played an important role in Arthur C. Clarke's story and Stanley Kubrick's associated film A Space Odyssey. In this, HAL 9000, a computer controlling spaceship Discovery One, bound for Jupiter, discovers a plot to deactivate it by the mission astronauts Dave Bowman and Frank Poole through lip reading their conversations.[12]

In Orson Scott Card’s series (including Ender’s Game), the artificial intelligence can be spoken to while the protagonist wears a movement sensor in his jaw, enabling him to converse with the AI without making noise. He also wears an ear implant.

Second Version:

A silent speech interface is a technology that translates thoughts or unspoken words into audible speech or written text without the need for vocalization. This innovative concept is often associated with brain-computer interface (BCI) technologies, which aim to establish direct communication between the human brain and external devices.

Here are some key aspects and technologies associated with silent speech interfaces:

  1. Electroencephalography (EEG): EEG is a non-invasive technique that measures electrical activity in the brain through electrodes placed on the scalp. Silent speech interfaces often use EEG to detect patterns of brain activity associated with speech production.
  2. Brain-Computer Interface (BCI): BCIs are systems that enable direct communication between the brain and external devices. In the context of silent speech interfaces, BCIs can interpret brain signals related to speech and convert them into text or synthesized speech.
  3. Machine Learning and Pattern Recognition: Advanced algorithms, including machine learning and pattern recognition, play a crucial role in decoding the complex patterns of brain activity associated with speech. These algorithms learn and adapt to individual users over time.
  4. Neurofeedback: Some silent speech interfaces incorporate neurofeedback mechanisms, allowing users to train and improve the system's accuracy by providing feedback on the system's interpretations of their thoughts.
  5. Electromyography (EMG): EMG measures electrical activity produced by muscle contractions. In silent speech interfaces, EMG sensors can be placed on facial muscles associated with speech production to detect subtle muscle movements related to unspoken words.
  6. Artificial Intelligence (AI) and Natural Language Processing (NLP): AI and NLP technologies are employed to convert decoded brain signals into meaningful text or speech. These technologies enhance the system's ability to understand and interpret the user's intentions.
  7. Applications: Silent speech interfaces have potential applications in various fields, including communication aids for individuals with speech disorders, hands-free communication in noisy environments, and covert communication in military or surveillance contexts.

It's important to note that while silent speech interfaces hold great promise, the technology is still in the early stages of development. Challenges include improving accuracy, addressing individual variability, and ensuring user privacy and security. Researchers and developers continue to explore and refine these interfaces to make them more practical and accessible for a broader range of users.

See also edit

References edit

  1. ^ Denby B, Schultz T, Honda K, Hueber T, Gilbert J.M., Brumberg J.S. (2010). Silent speech interfaces. Speech Communication 52: 270–287. doi:10.1016/j.specom.2009.08.002
  2. ^ a b Hueber T, Benaroya E-L, Chollet G, Denby B, Dreyfus G, Stone M. (2010). Development of a silent speech interface driven by ultrasound and optical images of the tongue and lips. Speech Communication, 52 288–300. doi:10.1016/j.specom.2009.11.004
  3. ^ Wang, J., Samal, A., & Green, J. R. (2014). Preliminary test of a real-time, interactive silent speech interface based on electromagnetic articulograph, the 5th ACL/ISCA Workshop on Speech and Language Processing for Assistive Technologies, Baltimore, MD, 38-45.
  4. ^ Jorgensen C, Dusan S. (2010). Speech interfaces based upon surface electromyography. Speech Communication, 52: 354–366. doi:10.1016/j.specom.2009.11.003
  5. ^ Schultz T, Wand M. (2010). Modeling Coarticulation in EMG-based Continuous Speech Recognition. Speech Communication, 52: 341-353. doi:10.1016/j.specom.2009.12.002
  6. ^ Hirahara T, Otani M, Shimizu S, Toda T, Nakamura K, Nakajima Y, Shikano K. (2010). Silent-speech enhancement using body-conducted vocal-tract resonance signals. Speech Communication, 52:301–313. doi:10.1016/j.specom.2009.12.001
  7. ^ Brumberg J.S., Nieto-Castanon A, Kennedy P.R., Guenther F.H. (2010). Brain–computer interfaces for speech communication. Speech Communication 52:367–379. 2010 doi:10.1016/j.specom.2010.01.001
  8. ^ Deng Y., Patel R., Heaton J. T., Colby G., Gilmore L. D., Cabrera J., Roy S. H., De Luca C.J., Meltzner G. S.(2009). Disordered speech recognition using acoustic and sEMG signals. In INTERSPEECH-2009, 644-647.
  9. ^ Deng Y., Colby G., Heaton J. T., and Meltzner HG. S. (2012). Signal Processing Advances for the MUTE sEMG-Based Silent Speech Recognition System. Military Communication Conference, MILCOM 2012.
  10. ^ Fitzpatrick M. (2002). Lip-reading cellphone silences loudmouths. New Scientist.
  11. ^ Wand M, Schultz T. (2011). Session-independent EMG-based Speech Recognition. Proceedings of the 4th International Conference on Bio-inspired Systems and Signal Processing.
  12. ^ Clarke, Arthur C. (1972). The Lost Worlds of 2001. London: Sidgwick and Jackson. ISBN 0-283-97903-8.

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