FindAPhD Weekly PhD Newsletter | JOIN NOW FindAPhD Weekly PhD Newsletter | JOIN NOW

Intelligent RF Sensing for Detecting Reliable Mechanical Vibration Signals from Human Vocal Folds for Speech Recognition


   Centre for Intelligent Healthcare

   Thursday, December 15, 2022  Competition Funded PhD Project (Students Worldwide)

About the Project

This PhD project is part of the Cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia.

The successful applicant will spend the 1st year at Coventry University and the following year at Deakin University and then the final 1.5 years at Coventry University

The supervision team will be drawn from the two Universities.

Research on the vibration of vocal folds is important for evaluating speech production and other associated speech signal processing areas, particularly, human phonation and voice disorders. Vocal fold vibration is a highly complicated, compact three-dimensional vibration. The observation and measurement of vocal fold vibrations using equipment such as electroglottographs (EGGs), video laryngoscopes , and high-speed video devices have been successfully applied for studying the motion of the vocal cord tissues. However, these methods cannot directly express the vocal cord motion characteristics and they must be applied to the throat, causing discomfort to patients. Certain external monitoring devices such as microphones have also been employed for acquiring acoustic signals, however, the recorded acoustic signals are easily disturbed by the surrounding background noise, which can degrade the signal quality considerably.

The electromagnetic (EM) radar sensor in tandem with biosignal processing techniques and state-of-the-art deep learning algorithms has been a promising alternative for various applications associated with phonation and can reliably address above mentioned research challenges due to its non-contact nature and the radiation that emits are far below the international standard.

In the Coventry University-led PhD project, we aim to develop a novel bio-signal processing techniques that can reliably record mechanical vibrations signal from vocal cords for long-term monitoring. Based on harmonic noise removal, novel filtering methods, and quantitative testing, innovative radar sensing technique will be developed, lab-tested, and initially validated on human subjects. The proposed methodology will improve the biocompatibility, accuracy, and reliability of the measurement towards good user experience.

  • Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates). 
  • A Masters degree in a relevant subject area, with overall mark at minimum Merit level. In addition, the mark for the Masters dissertation (or equivalent) must be a minimum of 80%. Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports. 
  • Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).  
  • The potential to engage in innovative research and to complete the PhD within a prescribed period of study.  

For an overview of each University’s entry requirements please visit:  

https://www.coventry.ac.uk/research/research-opportunities/research-students/cotutelle-phd-programmes/  

https://www.deakin.edu.au/research/become-a-research-student/research-degree-entry-pathways  

Please note that it is essential that applicants confirm that they are able to physically locate to both Coventry University (UK) and Deakin University (Australia)


Email Now


Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs