Silent-speech enhancement using body-conducted vocal-tract resonance signals

The physical characteristics of weak body-conducted vocal-tract resonance signals called non-audible murmur (NAM) and the acoustic characteristics of three sensors developed for detecting these signals have been investigated. NAM signals attenuate 50 dB at 1 kHz; this attenuation consists of 30-dB full-range attenuation due to air-to-body transmission loss and 10 dB/octave spectral decay due to a sound propagation loss within the body. These characteristics agree with the spectral characteristics of measured NAM signals. The sensors have a sensitivity of between 41 and 58 dB [V/Pa] at I kHz, and the mean signal-to-noise ratio of the detected signals was 15 dB. On the basis of these investigations, three types of silent-speech enhancement systems were developed: (1) simple, direct amplification of weak vocal-tract resonance signals using a wired urethane-elastomer NAM microphone, (2) simple, direct amplification using a wireless urethane-elastomer-duplex NAM microphone, and (3) transformation of the weak vocal-tract resonance signals sensed by a soft-silicone NAM microphone into whispered speech using statistical conversion. Field testing of the systems showed that they enable voice impaired people to communicate verbally using body-conducted vocal-tract resonance signals. Listening tests demonstrated that weak body-conducted vocal-tract resonance sounds can be transformed into intelligible whispered speech sounds. Using these systems, people with voice impairments can re-acquire speech communication with less effort. (C) 2009 Elsevier B.V. All rights reserved.ArticleSPEECH COMMUNICATION. 52(4):301-313 (2010)journal articl

Improving Body Transmitted Unvoiced Speech with Statistical Voice Conversion

INTERSPEECH2006: the 9th International Conference on Spoken Language Processing (ICSLP), September 17-21, 2006, Pittsburgh, Pennsylvania, USA.The conversion method from Non-Audible Murmur (NAM) to ordinary speech based on the statistical voice conversion (NAM-to-Speech) has been proposed towards realization of "silent speech telephone." Although NAM-to-Speech converts NAM to intelligible voices with similar quality to speech, there is still a large problem, i.e., difficulties of the F0 estimation from unvoiced speech. In order to avoid this problem, we propose a conversion method from NAM to whisper that is a familiar and intelligible unvoiced speech (NAM-to-Whisper). Moreover, we enhance NAM-to-Whisper so that multiple types of body-transmitted unvoiced speech such as NAM and Body Transmitted Whisper (BTW) are accepted as input voices. We evaluate the performance of the proposed conversion method. Experimental results demonstrate that 1) intelligibility and naturalness of NAM are significantly improved by NAM-to-Whisper, 2) NAM-to-Whisper outperforms NAM-to-Speech, and 3) we can train a single conversion model successfully converting both NAM and BTW to the target voice

EMG-to-Speech: Direct Generation of Speech from Facial Electromyographic Signals

The general objective of this work is the design, implementation, improvement and evaluation of a system that uses surface electromyographic (EMG) signals and directly synthesizes an audible speech output: EMG-to-speech

INTERSPEECH 2006- ICSLP Improving Body Transmitted Unvoiced Speech with Statistical Voice Conversion

The conversion method from Non-Audible Murmur (NAM) to ordinary speech based on the statistical voice conversion (NAM-to-Speech) has been proposed towards realization of “silent speech telephone. ” Although NAM-to-Speech converts NAM to intelligible voices with similar quality to speech, there is still a large problem, i.e., difficulties of the F0 estimation from unvoiced speech. In order to avoid this problem, we propose a conversion method from NAM to whisper that is a familiar and intelligible unvoiced speech (NAM-to-Whisper). Moreover, we enhance NAM-to-Whisper so that multiple types of body-transmitted unvoiced speech such as NAM and Body Transmitted Whisper (BTW) are accepted as input voices. We evaluate the performance of the proposed conversion method. Experimental results demonstrate that 1) intelligibility and naturalness of NAM are significantly improved by NAM-to-Whisper, 2) NAM-to-Whisper outperforms NAM-to-Speech, and 3) we can train a single conversion model successfully converting both NAM and BTW to the target voice. Index Terms: silent speech telephone, body transmitted unvoiced speech, voice conversion, F0 estimation, whispe