Flexible piezoresistive sensors with a porous structure that are used in the field of speech recognition are seldom characterized by both high sensitivity and ease of preparation. In this study, a piezoresistive sensor with a porous structure that is both highly sensitive and can be prepared by using a simple method is proposed for speech recognition. The preparation process utilizes the interaction of bubbles generated by ethanol evaporation and active agents with polydimethylsiloxane to produce a porous flexible substrate. This preparation process requires neither templates nor harsh experimental conditions such as a low temperature and a low pressure. Furthermore, the prepared piezoresistive sensor has excellent properties, such as a high sensitivity (27.6 kPa), a satisfactory response time (800 μs), and a good stability (10,000 cycles). When used for speech recognition, more than 1500 vocalizations and silent speech signals obtained from subjects saying numbers from "0" to "9" were collected by the sensor for training a convolutional neural network model. The average accuracy of the recognition reached 94.8%. The simple preparation process and the excellent performance of the prepared flexible piezoresistive sensor endow it with a wide application prospect in the field of speech recognition.
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http://dx.doi.org/10.1021/acsami.3c18233 | DOI Listing |
Front Neurosci
January 2025
The Basic Department, The Tourism College of Changchun University, Changchun, China.
Introduction: In the field of medical listening assessments,accurate transcription and effective cognitive load management are critical for enhancing healthcare delivery. Traditional speech recognition systems, while successful in general applications often struggle in medical contexts where the cognitive state of the listener plays a significant role. These conventional methods typically rely on audio-only inputs and lack the ability to account for the listener's cognitive load, leading to reduced accuracy and effectiveness in complex medical environments.
View Article and Find Full Text PDFData Brief
February 2025
Computer Science Department, College of Science, University of Baghdad, Iraq.
The availability of raw data is a considerable challenge across most branches of science. In the absence of data, neither experiments can be conducted nor development can be undertaken. Despite their importance, raw data are still lacking across many scientific fields.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
USC Viterbi School of Engineering, University of Southern California, Los Angeles, California 90089-1455, USA.
Voice quality serves as a rich source of information about speakers, providing listeners with impressions of identity, emotional state, age, sex, reproductive fitness, and other biologically and socially salient characteristics. Understanding how this information is transmitted, accessed, and exploited requires knowledge of the psychoacoustic dimensions along which voices vary, an area that remains largely unexplored. Recent studies of English speakers have shown that two factors related to speaker size and arousal consistently emerge as the most important determinants of quality, regardless of who is speaking.
View Article and Find Full Text PDFData Brief
February 2025
Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology (MIST), Dhaka 1216, Bangladesh.
The dataset represents a significant advancement in Bengali lip-reading and visual speech recognition research, poised to drive future applications and technological progress. Despite Bengali's global status as the seventh most spoken language with approximately 265 million speakers, linguistically rich and widely spoken languages like Bengali have been largely overlooked by the research community. fills this gap by offering a pioneering dataset tailored for Bengali lip-reading, comprising visual data from 150 speakers across 54 classes, encompassing Bengali phonemes, alphabets, and symbols.
View Article and Find Full Text PDFInt J Audiol
January 2025
Department of Otorhinolaryngology and Head & Neck Surgery, Leiden University Medical Center, Leiden, Netherlands.
Objective: Measuring listening effort using pupillometry is challenging in cochlear implant (CI) users. We assess three validated speech tests (Matrix, LIST, and DIN) to identify the optimal speech material for measuring peak-pupil-dilation (PPD) in CI users as a function of signal-to-noise ratio (SNR).
Design: Speech tests were administered in quiet and two noisy conditions, namely at the speech recognition threshold (0 dB re SRT), i.
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