A Weighted STOI Intelligibility Metric Based On Mutual Information

It is known that the information required for the intelligibility of a speech signal is distributed non-uniformly in time. In this paper we propose WSTOI, a modified version of STOI, a speech intelligibility metric. With WSTOI the contribution of each time-frequency cell is weighted by an estimate of its intelligibility content. This estimate is equal to the mutual information between two hypothetical signals at either end of a simplified model of human communication. Listening tests show that the modification improves the prediction accuracy of STOI at all performance levels on both long and short utterances. An improvement was observed across all tested noise types and suppression algorithms

Improving the perceptual quality of ideal binary masked speech

It is known that applying a time-frequency binary mask to very noisy speech can improve its intelligibility but results in poor perceptual quality. In this paper we propose a new approach to applying a binary mask that combines the intelligibility gains of conventional binary masking with the perceptual quality gains of a classical speech enhancer. The binary mask is not applied directly as a time-frequency gain as in most previous studies. Instead, the mask is used to supply prior information to a classical speech enhancer about the probability of speech presence in different time-frequency regions. Using an oracle ideal binary mask, we show that the proposed method results in a higher predicted quality than other methods of applying a binary mask whilst preserving the improvements in predicted intelligibility

The Development and Implementation of ALIGN: A Multidimensional Program Designed to Enhance the Success of Black, Indigenous, and People of Color (BIPOC) Graduate Students in Communication Sciences and Disorders

The critical lack of racially and ethnically diverse healthcare professionals in the field of Communication Sciences & Disorders (CSD) in contrast to the increasing diversity of the U.S. population may contribute to healthcare disparities and negatively impact healthcare outcomes. It is therefore imperative for transformational programs and practices to be enacted to substantially increase the number of CSD professionals representing Black, Indigenous, and People of Color (BIPOC). As training institutions that graduate and contribute to the certification of CSD professionals, universities are fundamental for contributing to this change. Numerous barriers have been identified that limit the number of underrepresented minority students who matriculate in and graduate from speech-language pathology and audiology graduate programs. At Syracuse University, a group of academic and clinical CSD faculty developed a program to specifically address these barriers: the Academic Skill Building and Networking (ALIGN) program. ALIGN implements a multifaceted approach toward facilitating the success of CSD BIPOC graduate students through the integration of academic and professional skill building, peer mentoring and networking, and professional mentoring and networking into the program curriculum. This study described the rationale and development of the ALIGN program, and reported quantitative and qualitative survey results to determine the preliminary effects of this program on an inaugural cohort of ALIGN participants. Overall, quantitative and qualitative data indicated that ALIGN had a substantial, positive impact on academic skills relative to study habits, understanding difficult course concepts, and general learning, and provided crucial support and connection opportunities with fellow BIPOC students

Neonatal cerebrovascular autoregulation.

Cerebrovascular pressure autoregulation is the physiologic mechanism that holds cerebral blood flow (CBF) relatively constant across changes in cerebral perfusion pressure (CPP). Cerebral vasoreactivity refers to the vasoconstriction and vasodilation that occur during fluctuations in arterial blood pressure (ABP) to maintain autoregulation. These are vital protective mechanisms of the brain. Impairments in pressure autoregulation increase the risk of brain injury and persistent neurologic disability. Autoregulation may be impaired during various neonatal disease states including prematurity, hypoxic-ischemic encephalopathy (HIE), intraventricular hemorrhage, congenital cardiac disease, and infants requiring extracorporeal membrane oxygenation (ECMO). Because infants are exquisitely sensitive to changes in cerebral blood flow (CBF), both hypoperfusion and hyperperfusion can cause significant neurologic injury. We will review neonatal pressure autoregulation and autoregulation monitoring techniques with a focus on brain protection. Current clinical therapies have failed to fully prevent permanent brain injuries in neonates. Adjuvant treatments that support and optimize autoregulation may improve neurologic outcomes

Tiger trails in southern Asia,

Mode of access: Internet

Binaural mask-informed speech enhancement for hearing aids with head tracking

An end-to-end speech enhancement system for hearing aids is pro- posed which seeks to improve the intelligibility of binaural speech in noise during head movement. The system uses a reference beam- former whose look direction is informed by knowledge of the head orientation and the a priori known direction of the desired source. From this a time-frequency mask is estimated using a deep neural network. The binaural signals are obtained using bilateral beam- formers followed by a classical minimum mean square error speech enhancer, modified to use the estimated mask as a speech presence probability prior. In simulated experiments, the improvement in a binaural intelligibility metric (DBSTOI) given by the proposed sys- tem relative to beamforming alone corresponds to an SNR improve- ment of 4 to 6 dB. Results also demonstrate the individual contribu- tions of incorporating the mask and the head orientation-aware beam steering to the proposed system

Improving the perceptual quality of ideal binary masked speech

It is known that applying a time-frequency binary mask to very noisy speech can improve its intelligibility but results in poor perceptual quality. In this paper we propose a new approach to applying a binary mask that combines the intelligibility gains of conventional binary masking with the perceptual quality gains of a classical speech enhancer. The binary mask is not applied directly as a time-frequency gain as in most previous studies. Instead, the mask is used to supply prior information to a classical speech enhancer about the probability of speech presence in different time-frequency regions. Using an oracle ideal binary mask, we show that the proposed method results in a higher predicted quality than other methods of applying a binary mask whilst preserving the improvements in predicted intelligibility

Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement

A signal processing approach combining beamforming with mask-informed speech enhancement was assessed by measuring sentence recognition in listeners with mild-to-moderate hearing impairment in adverse listening conditions that simulated the output of behind-the-ear hearing aids in a noisy classroom. Two types of beamforming were compared: binaural, with the two microphones of each aid treated as a single array, and bilateral, where independent left and right beamformers were derived. Binaural beamforming produces a narrower beam, maximising improvement in signal-to-noise ratio (SNR), but eliminates the spatial diversity that is preserved in bilateral beamforming. Each beamformer type was optimised for the true target position and implemented with and without additional speech enhancement in which spectral features extracted from the beamformer output were passed to a deep neural network trained to identify time-frequency regions dominated by target speech. Additional conditions comprising binaural beamforming combined with speech enhancement implemented using Wiener filtering or modulation-domain Kalman filtering were tested in normally-hearing (NH) listeners. Both beamformer types gave substantial improvements relative to no processing, with significantly greater benefit for binaural beamforming. Performance with additional mask-informed enhancement was poorer than with beamforming alone, for both beamformer types and both listener groups. In NH listeners the addition of mask-informed enhancement produced significantly poorer performance than both other forms of enhancement, neither of which differed from the beamformer alone. In summary, the additional improvement in SNR provided by binaural beamforming appeared to outweigh loss of spatial information, while speech understanding was not further improved by the mask-informed enhancement method implemented here