Background speaker noise removal using combined EM sensor/acoustic signals

Abstract

Recently, very low-power EM radarlike sensors have been used to measure the macro- and micro-motions of human speech articulators as human speech is produced [see Holzrichter et al., J. Acoust. Soc. Am. 103, 622 (1998)]. These sensors can measure tracheal wall motions, associated with the air pressure build up and fall as the vocal folds open and close, leading to a voiced speech excitation function. In addition, they provide generalized motion measurements of vocal tract articulator gestures that lead to speech formation. For example, tongue, jaw, lips, velum, and pharynx motions have been measured as speech is produced. Since the EM sensor information is independent of acoustic air pressure waves, it is independent of the state of the acoustic background noise spectrum surrounding the speaker. By correlating the two streams of information together, from a microphone and (one or more) EM sensor signals, to characterize a speaker’s speech signal, much of the background speaker noise can be eliminated in real time. This paper presents several algorithms to demonstrate the added noise suppression capability of the glottal EM sensors (GEMS). [Work supported by NSF and DOE.]