Background: Children with hearing loss frequently experience difficulty understanding speech in the presence of noise. Although remote microphone systems are likely to be the most effective solution to improve speech recognition in noise, the focus of this study centers on the evaluation of hearing aid noise management technologies including directional microphones, adaptive noise reduction (ANR), and frequency-gain shaping. These technologies can improve children's speech recognition, listening comfort, and/or sound quality in noise. However, individual contributions of these technologies as well as the effect of hearing aid microphone mode on localization abilities in children is unknown.
Purpose: The objectives of this study were to (1) compare children's speech recognition and subjective perceptions across five hearing aid noise management technology conditions and (2) compare localization abilities across three hearing aid microphone modes.
Research Design: A single-group, repeated measures design was used to evaluate performance differences and subjective ratings.
Study Sample: Fourteen children with mild to moderately severe hearing loss.
Data Collection And Analysis: Children's sentence recognition, listening comfort, sound quality, and localization were assessed in a room with an eight-loudspeaker array.
Results And Conclusion: The use of adaptive directional microphone technology improves children's speech recognition in noise when the signal of interest arrives from the front and is spatially separated from the competing noise. In contrast, the use of adaptive directional microphone technology may result in a decrease in speech recognition in noise when the signal of interest arrives from behind. The use of a microphone mode that mimics the natural directivity of the unaided auricle provides a slight improvement in speech recognition in noise compared with omnidirectional use with limited decrement in speech recognition in noise when the signal of interest arrives from behind. The use of ANR and frequency-gain shaping provide no change in children's speech recognition in noise. The use of adaptive directional microphone technology, ANR, and frequency-gain shaping improve children's listening comfort, perceived ability to understand speech in noise, and overall listening experience. Children prefer to use each of these noise management technologies regardless of whether the signal of interest arrives from the front or from behind. The use of adaptive directional microphone technology does not result in a decrease in children's localization abilities when compared with the omnidirectional condition. The best localization performance occurred with use of the microphone mode that mimicked the directivity of the unaided auricle.
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http://dx.doi.org/10.1055/s-0041-1735802 | DOI Listing |
J Speech Lang Hear Res
January 2025
Centre for Language Studies, Radboud University, Nijmegen, the Netherlands.
Purpose: In this review article, we present an extensive overview of recent developments in the area of dysarthric speech research. One of the key objectives of speech technology research is to improve the quality of life of its users, as evidenced by the focus of current research trends on creating inclusive conversational interfaces that cater to pathological speech, out of which dysarthric speech is an important example. Applications of speech technology research for dysarthric speech demand a clear understanding of the acoustics of dysarthric speech as well as of speech technologies, including machine learning and deep neural networks for speech processing.
View Article and Find Full Text PDFJ Neurol
January 2025
Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Praha 6, 16000, Prague, Czech Republic.
Background And Objectives: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD.
Methods: Spontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing.
Ophthalmologie
January 2025
Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland.
Background: The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide a solution.
Objective: The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization.
Dev Sci
March 2025
MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Sydney, Australia.
The classical view is that perceptual attunement to the native language, which emerges by 6-10 months, developmentally precedes phonological feature abstraction abilities. That assumption is challenged by findings from adults adopted into a new language environment at 3-5 months that imply they had already formed phonological feature abstractions about their birth language prior to 6 months. As phonological feature abstraction had not been directly tested in infants, we examined 4-6-month-olds' amodal abstraction of the labial versus coronal place of articulation distinction between consonants.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mathematics and Computer Science, Tongling University, Tongling, 244061, China.
The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs.
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