Noise Suppression with Non-Air-Acoustic Sensors

Nonacoustic sensors such as the general electromagnetic motion sensor (GEMS), the physiological microphone (P-Mic), and the electroglottograph (EGG) offer multimodal approaches to speech processing and speaker and speech recognition. These sensors provide measurements of functions of the glottal excitation and, more generally, of the vocal tract articulator movements that are relatively immune to acoustic disturbances and can supplement the acoustic speech waveform. This thesis describes an approach to speech enhancement that exploits these nonacoustic sensors according to their capability in representing specific speech characteristics in different frequency bands. Frequency-domain sensor phase, as well as magnitude, is found to contribute to signal enhancement. Testing involves the time-synchronous multi-sensor DARPA Advanced Speech Encoding corpus collected in a variety of harsh acoustic noise environments. The enhancement approach is illustrated with examples that indicate its applicability as a pre-processor to low-rate vocoding and speaker authentication, and for enhanced listening from degraded speech. Thesis Supervisor: Dr. Thomas F. Quatieri Title: Senior Technical Staff, MIT Lincoln Laboratory