Digital processing of speech signals

A system for detecting non-verbal acoustic energy generated by a subject is provided. The system includes a sensor mountable on or in a body region of the subject, the sensor being capable of sensing the non-verbal acoustic energy; and a processing unit being capable of processing the non-verbal acoustic energy sensed by the sensor and deriving an activity related signature therefrom, thereby enabling identification of a specific activity associated with the non-verbal acoustic energy.

Systems and Methods for Monitoring and Modifying Behavior

Applied research on biomedical applications of UWB radar is targeted to the identification of the possible new devices made possible by the technology, to the design and development of those devices, and to the clinical testing of the systems obtained. Applications can be divided into two main sectors according to the frequency range used. For the conventional UWB radar microwave region: cardiac biomechanics assessment; chest movements assessment OSA (obstructive sleep apnoea) monitors; soft-tissue biomechanics research; heart imaging ("Holter type" echocardiography); chest imaging. Together with systems for: cardiac monitoring; respiratory monitoring; SIDS (sudden infant death syndrome) monitor; vocal tract studying. If an IR laser diode is used as the antenna, a more common radar is obtained (actually a hybrid between a narrow band and a wide band radar) which emits a short packet of electromagnetic waves whose echoes are sampled using conventional UWB receiver equipped with a PIN photodiode. Possibilities include: non-invasive biochemical study of soft tissues, non-invasive study of metabolic processes, and IR spectral imaging.

UWB radars in medicine

https://www.neurones.espci.fr/Articles_PS/SPEECHCOM%201.pdf

Silent speech interfaces

A cooperative conversational voice user interface is provided. The cooperative conversational voice user interface may build upon short-term and long-term shared knowledge to generate one or more explicit and/or implicit hypotheses about an intent of a user utterance. The hypotheses may be ranked based on varying degrees of certainty, and an adaptive response may be generated for the user. Responses may be worded based on the degrees of certainty and to frame an appropriate domain for a subsequent utterance. In one implementation, misrecognitions may be tolerated, and conversational course may be corrected based on subsequent utterances and/or responses.

System and method for a cooperative conversational voice user interface

Communication systems are described, including both portable handset and headset devices, which use a number of microphone configurations to receive acoustic signals of an environment. The microphone configurations include, for example, a two-microphone array including two unidirectional microphones, and a two-microphone array including one unidirectional microphone and one omnidirectional microphone. The communication systems also include Voice Activity Detection (VAD) devices to provide information of human voicing activity. Components of the communications systems receive the acoustic signals and voice activity signals and, in response, automatically generate control signals from data of the voice activity signals. Components of the communication systems use the control signals to automatically select a denoising method appropriate to data of frequency subbands of the acoustic signals. The selected denoising method is applied to the acoustic signals to generate denoised acoustic signals when the acoustic signal includes speech (101) and noise (102).

Microphone and voice activity detection (vad) configurations for use with communication systems

Systems and methods are provided for detecting voiced and unvoiced speech in acoustic signals having varying levels of background noise. The systems receive acoustic signals at two microphones, and generate difference parameters between the acoustic signals received at each of the two microphones. The difference parameters are representative of the relative difference in signal gain between portions of the received acoustic signals. The systems identify information of the acoustic signals as unvoiced speech when the difference parameters exceed a first threshold, and identify information of the acoustic signals as voiced speech when the difference parameters exceed a second threshold. Further, embodiments of the systems include non-acoustic sensors that receive physiological information to aid in identifying voiced speech.

Detecting voiced and unvoiced speech using both acoustic and nonacoustic sensors

Acoustic noise suppression is provided in multiple-microphone systems using Voice Activity Detectors (VAD). A host system receives acoustic signals via multiple microphones. The system also receives information on the vibration of human tissue associated with human voicing activity via the VAD. In response, the system generates a transfer function representative of the received acoustic signals upon determining that voicing information is absent from the received acoustic signals during at least one specified period of time. The system removes noise from the received acoustic signals using the transfer function, thereby producing a denoised acoustic data stream.

Voice activity detector (vad) -based multiple-microphone acoustic noise suppression

Very low power electromagnetic (EM) wave sensors are being used to measure speech articulator motions as speech is produced. Glottal tissue oscillations, jaw, tongue, soft palate, and other organs have been measured. Previously, microwave imaging (e.g., using radar sensors) appears not to have been considered for such monitoring. Glottal tissue movements detected by radar sensors correlate well with those obtained by established laboratory techniques, and have used to estimate a voiced excitation function for speech processing applications. The noninvasive access, coupled with the small size, low power, and high resolution of these new sensors, permit promising research and development applications in speech production, communication disorders, speech recognition and related topics.

Speech articulator measurements using low power EM-wave sensors

Systems and methods to reduce the negative impact of wind on an electronic system include use of a first detector that receives a first signal and a second detector that receives a second signal. A voice activity detector (VAD) coupled to the first detector generates a VAD signal when the first signal corresponds to voiced speech. A wind detector coupled to the second detector correlates signals received at the second detector and derives from the correlation wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector. The wind detector controls a configuration of the second detector according to the wind metrics. The wind detector uses the wind metrics to dynamically control mixing of the first signal and the second signal to generate an output signal for transmission.

Gregory C. Burnett

Papers

Microphone and voice activity detection (vad) configurations for use with communication systems

Detecting voiced and unvoiced speech using both acoustic and nonacoustic sensors

Voice activity detector (vad) -based multiple-microphone acoustic noise suppression

Speech articulator measurements using low power EM-wave sensors

Wind suppression/replacement component for use with electronic systems