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Silent speech interfaces have been pursued to restore spoken communication for individuals with voice disorders and to facilitate intuitive communications when acoustic-based speech communication is unreliable, inappropriate, or undesired. However, the current methodology for silent speech faces several challenges, including bulkiness, obtrusiveness, low accuracy, limited portability, and susceptibility to interferences. In this work, we present a wireless, unobtrusive, and robust silent speech interface for tracking and decoding speech-relevant movements of the temporomandibular joint. Our solution employs a single soft magnetic skin placed behind the ear for wireless and socially acceptable silent speech recognition. The developed system alleviates several concerns associated with existing interfaces based on face-worn sensors, including a large number of sensors, highly visible interfaces on the face, and obtrusive interconnections between sensors and data acquisition components. With machine learning-based signal processing techniques, good speech recognition accuracy is achieved (93.2% accuracy for phonemes, and 87.3% for a list of words from the same viseme groups). Moreover, the reported silent speech interface demonstrates robustness against noises from both ambient environments and users’ daily motions. Finally, its potential in assistive technology and human–machine interactions is illustrated through two demonstrations – silent speech enabled smartphone assistants and silent speech enabled drone control.
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Electromyogram‐Based Lip‐Reading via Unobtrusive Dry Electrodes and Machine Learning Methods
Abstract Lip‐reading provides an effective speech communication interface for people with voice disorders and for intuitive human–machine interactions. Existing systems are generally challenged by bulkiness, obtrusiveness, and poor robustness against environmental interferences. The lack of a truly natural and unobtrusive system for converting lip movements to speech precludes the continuous use and wide‐scale deployment of such devices. Here, the design of a hardware–software architecture to capture, analyze, and interpret lip movements associated with either normal or silent speech is presented. The system can recognize different and similar visemes. It is robust in a noisy or dark environment. Self‐adhesive, skin‐conformable, and semi‐transparent dry electrodes are developed to track high‐fidelity speech‐relevant electromyogram signals without impeding daily activities. The resulting skin‐like sensors can form seamless contact with the curvilinear and dynamic surfaces of the skin, which is crucial for a high signal‐to‐noise ratio and minimal interference. Machine learning algorithms are employed to decode electromyogram signals and convert them to spoken words. Finally, the applications of the developed lip‐reading system in augmented reality and medical service are demonstrated, which illustrate the great potential in immersive interaction and healthcare applications.
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- Award ID(s):
- 2129673
- PAR ID:
- 10409521
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Small
- Volume:
- 19
- Issue:
- 17
- ISSN:
- 1613-6810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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