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Purpose: Prosody perception is an essential component of speech communication and social interaction through which both linguistic and emotional information are conveyed. Considering the importance of the auditory system in processing prosody-related acoustic features, the aim of this review article is to review the effects of hearing impairment on prosody perception in children and adults. It also assesses the performance of hearing assistive devices in restoring prosodic perception. Method: Following a comprehensive online database search, two lines of inquiry were targeted. The first summarizes recent attempts toward determining the effects of hearing loss and interacting factors such as age and cognitive resources on prosody perception. The second analyzes studies reporting beneficial or detrimental impacts of hearing aids, cochlear implants, and bimodal stimulation on prosodic abilities in people with hearing loss. Results: The reviewed studies indicate that hearing-impaired individuals vary widely in perceiving affective and linguistic prosody, depending on factors such as hearing loss severity, chronological age, and cognitive status. In addition, most of the emerging information points to limitations of hearing assistive devices in processing and transmitting the acoustic features of prosody. Conclusions: The existing literature is incomplete in several respects, including the lack of a consensus on how and to what extent hearing prostheses affect prosody perception, especially the linguistic function of prosody, and a gap in assessing prosody under challenging listening situations such as noise. This review article proposes directions that future research could follow to provide a better understanding of prosody processing in those with hearing impairment, which may help health care professionals and designers of assistive technology to develop innovative diagnostic and rehabilitation tools.

This study introduces an improved method to investigate the effects of reverberation using the speech-evoked auditory brainstem response (ABR) that more realistically captures the influence of self- and overlap-masking induced by room reverberation. Speech-evoked ABR was measured under three acoustic scenarios: anechoic, mild reverberation with dominance of early reflections, and severe reverberation with dominance of late reverberation. Responses were significantly weaker and had longer latencies with severe reverberation relative to anechoic and mild reverberation. Although larger responses and shorter latencies were observed with mild reverberation than anechoic, possibly due to early reflections, these reached significance in only one of six ABR response measures. [ABSTRACT FROM AUTHOR]

Objectives: The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified. Methods: A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water. Results: Our proposed pattern classification system achieved accuracies of up to $93$% in stationary subject detection, $90$% in stop-breathing detection, and $86$% in interference detection. Conclusion: Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences. Significance: Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection. [ABSTRACT FROM AUTHOR]