- Arthur R. Toth, Michael Wand, S. Jou, Tanja Schultz, B. Raj, Kaustubh Kalgaonkar, Tony Ezzat. 2010. Synthesizing speech from surface electromyography and acoustic Doppler sonar.. Abstract: Numerous techniques have been devised to process speech audio in noise, but automatic speech recognition is difficult when the noise is too great. An alternative approach is to collect data that represent the speech production process but is less affected by noise in the speech audio range. Two such types of data come from surface electromyography (EMG) and acoustic Doppler sonar (ADS). EMG records muscle activation potentials. ADS records reflected ultrasound tones. Both can be used to measure facial movements related to speech, but they present their own challenges for automatic speech recognition. This work investigates the alternative approach of using these data sources for speech synthesis. The synthesis techniques explored in this work are based on Gaussian mixture model mapping techniques, which are commonly used for voice transformation. Voice transformation is traditionally concerned with changing the identity of speech audio signals, but others have demonstrated that such techniques can be used...
