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Journal articles on the topic 'Speech intelligibility enhancement'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Nemade, Milind U., and Satish K. Shah. "Speech Enhancement Techniques: Quality vs. Intelligibility." International Journal of Future Computer and Communication 3, no. 3 (2014): 216–21. http://dx.doi.org/10.7763/ijfcc.2014.v3.299.

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2

Kates, James M. "Speech intelligibility enhancement." Journal of the Acoustical Society of America 83, no. 6 (1988): 2474. http://dx.doi.org/10.1121/1.396313.

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Yang, Yu Xiang, and Jian Fen Ma. "Speech Intelligibility Enhancement Using Distortion Control." Advanced Materials Research 912-914 (April 2014): 1391–94. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1391.

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In order to improve the intelligibility of the noisy speech, a novel speech enhancement algorithm using distortion control is proposed. The reason why current speech enhancement algorithm cannot improve speech intelligibility is that these algorithms aim to minimize the overall distortion of the enhanced speech. However, different speech distortions make different contributions to the speech intelligibility. The distortion in excess of 6.02dB has the most detrimental effects on speech intelligibility. In the process of noise reduction, the type of speech distortion can be determined by signal

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Liu, Peng, and Jian Fen Ma. "A Higher Intelligibility Speech-Enhancement Algorithm." Applied Mechanics and Materials 321-324 (June 2013): 1075–79. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1075.

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A higher intelligibility speech-enhancement algorithm based on subspace is proposed. The majority existing speech-enhancement algorithms cannot effectively improve enhanced speech intelligibility. One important reason is that they only use Minimum Mean Square Error (MMSE) to constrain speech distortion but ignore that speech distortion region differences have a significant effect on intelligibility. A priori Signal Noise Ratio (SNR) and gain matrix were used to determine the distortion region. Then the gain matrix was modified to constrain the magnitude spectrum of the amplification distortion

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Yi, Astrid, Willy Wong, and Moshe Eizenman. "Gaze Patterns and Audiovisual Speech Enhancement." Journal of Speech, Language, and Hearing Research 56, no. 2 (2013): 471–80. http://dx.doi.org/10.1044/1092-4388(2012/10-0288).

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Purpose In this study, the authors sought to quantify the relationships between speech intelligibility (perception) and gaze patterns under different auditory–visual conditions. Method Eleven subjects listened to low-context sentences spoken by a single talker while viewing the face of one or more talkers on a computer display. Subjects either maintained their gaze at a specific distance (0°, 2.5°, 5°, 10°, and 15°) from the center of the talker's mouth (CTM) or moved their eyes freely on the computer display. Eye movements were monitored with an eye-tracking system, and speech intelligibility

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Giri, Mahesh, and Neela Rayavarapu. "Improving the intelligibility of dysarthric speech using a time domain pitch synchronous-based approach." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4041. http://dx.doi.org/10.11591/ijece.v13i4.pp4041-4051.

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Dysarthria is a motor speech impairment that reduces the intelligibility of speech. Observations indicate that for different types of dysarthria, the fundamental frequency, intensity, and speech rate of speech are distinct from those of unimpaired speakers. Therefore, the proposed enhancement technique modifies these parameters so that they fall in the range for unimpaired speakers. The fundamental frequency and speech rate of dysarthric speech are modified using the time domain pitch synchronous overlap and add (TD-PSOLA) algorithm. Then its intensity is modified using the fast Fourier transf

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Mahesh, Giri, and Rayavarapu Neela. "Improving the intelligibility of dysarthric speech using a time domain pitch synchronous-based approach." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4041–51. https://doi.org/10.11591/ijece.v13i4.pp4041-4051.

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Dysarthria is a motor speech impairment that reduces the intelligibility of speech. Observations indicate that for different types of dysarthria, the fundamental frequency, intensity, and speech rate of speech are distinct from those of unimpaired speakers. Therefore, the proposed enhancement technique modifies these parameters so that they fall in the range for unimpaired speakers. The fundamental frequency and speech rate of dysarthric speech are modified using the time domain pitch synchronous overlap and add (TD-PSOLA) algorithm. Then its intensity is modified using the fast Fourier transf

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Ghorpade, Kalpana, and Arti Khaparde. "Single-channel speech enhancement by PSO-GSA with harmonic regeneration noise reduction." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 2895–902. http://dx.doi.org/10.11591/eei.v12i5.5373.

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Speech quality significantly affects the performance of speech dependent systems. Noise in the background lowers the clarity and intelligibility of speech. The augmentation of speech can increase its quality. We propose a single-channel speech improvement framework that combines particle swarm optimization (PSO), gravitational search algorithm (GSA), and harmonic regeneration noise reduction (HRNR) to minimize speech signal noise and increase speech intelligibility. The proposed hybrid algorithm optimizes the amount of overlap between the noisy speech frames. This helps in reducing the overlap

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Shahidi, Lidea K., Leslie M. Collins, and Boyla O. Mainsah. "Objective intelligibility measurement of reverberant vocoded speech for normal-hearing listeners: Towards facilitating the development of speech enhancement algorithms for cochlear implants." Journal of the Acoustical Society of America 155, no. 3 (2024): 2151–68. http://dx.doi.org/10.1121/10.0025285.

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Cochlear implant (CI) recipients often struggle to understand speech in reverberant environments. Speech enhancement algorithms could restore speech perception for CI listeners by removing reverberant artifacts from the CI stimulation pattern. Listening studies, either with cochlear-implant recipients or normal-hearing (NH) listeners using a CI acoustic model, provide a benchmark for speech intelligibility improvements conferred by the enhancement algorithm but are costly and time consuming. To reduce the associated costs during algorithm development, speech intelligibility could be estimated

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Majewski, Wojciech J. "Aural method of speech intelligibility enhancement." Journal of the Acoustical Society of America 103, no. 5 (1998): 2772. http://dx.doi.org/10.1121/1.421402.

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Kollmeier, B., and J. Peissig. "Speech Intelligibility Enhancement by Interaural Magnification." Acta Oto-Laryngologica 109, sup469 (1990): 215–23. http://dx.doi.org/10.1080/00016489.1990.12088432.

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Deux, Florent, and Mendel Kleiner. "Binaural enhancement of speech intelligibility metrics." Journal of the Acoustical Society of America 123, no. 5 (2008): 3608. http://dx.doi.org/10.1121/1.2934792.

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Ghorpade, Kalpana, and Arti Khaparde. "SINGLE CHANNEL SPEECH ENHANCEMENT USING EVOLUTIONARY ALGORITHM WITH LOG-MMSE." ASEAN Engineering Journal 12, no. 1 (2022): 83–91. http://dx.doi.org/10.11113/aej.v12.16770.

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Additive noise degrades speech quality and intelligibility. Speech enhancement reduces this noise to make speech more pleasant and intelligible. It plays a significant role in speech recognition or speech-operated systems. In this paper, we propose a single-channel speech enhancement method in which the log-minimum mean square error method (log-MMSE) and modified accelerated particle swarm optimization algorithm are used to design a filter for improving the quality and intelligibility of noisy speech. Accelerated particle swarm optimization (APSO) algorithm is modified in which a single dimens

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Zhang, Yunqi C., Yusuke Hioka, C. T. Justine Hui, and Catherine I. Watson. "Performance of speech enhancement algorithms on the speech intelligibility of native Mandarin listeners immersed in English-speaking environment." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 8 (2023): 397–402. http://dx.doi.org/10.3397/in_2023_0071.

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Speech enhancement algorithms have been developed to improve speech intelligibility for listeners under noisy conditions. However, all existing algorithms were evaluated only by native listeners, the performance of such algorithms on non-native listeners was rarely investigated. This study conducts a subjective listening test on native New Zealand English listeners and native Mandarin listeners who have been immersed in New Zealand English-speaking environment for more than one year. The participants were asked to transcribe noisy English sentences processed by five widely used single-channel

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Dachasilaruk, Siriporn, Niphat Jantharamin, and Apichai Rungruang. "Speech intelligibility enhancement for Thai-speaking cochlear implant listeners." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 866. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp866-875.

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Cochlear implant (CI) listeners encounter difficulties in communicating with other persons in noisy listening environments. However, most CI research has been carried out using the English language. In this study, single-channel speech enhancement (SE) strategies as a pre-processing approach for the CI system were investigated in terms of Thai speech intelligibility improvement. Two SE algorithms, namely multi-band spectral subtraction (MBSS) and Weiner filter (WF) algorithms, were evaluated. Speech signals consisting of monosyllabic and bisyllabic Thai words were degraded by speech-shaped noi

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Siriporn, Dachasilaruk, Jantharamin Niphat, and Rungruang Apichai. "Speech intelligibility enhancement for Thai-speaking cochlear implant listeners." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 866–75. https://doi.org/10.11591/ijeecs.v13.i3.pp866-875.

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Cochlear implant (CI) listeners encounter difficulties in communicating with other persons in noisy listening environments. However, most CI research has been carried out using the English language. In this study, single-channel speech enhancement (SE) strategies as a pre-processing approach for the CI system were investigated in terms of Thai speech intelligibility improvement. Two SE algorithms, namely multi-band spectral subtraction (MBSS) and Weiner filter (WF) algorithms, were evaluated. Speech signals consisting of monosyllabic and bisyllabic Thai words were degraded by speech-shaped noi

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Smriti, Sahu, and Rayavarapu Neela. "Compressive speech enhancement using semi-soft thresholding and improved threshold estimation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2788–800. https://doi.org/10.11591/ijece.v13i3.pp2788-2800.

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Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basisfunction based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silenceregion of the signa

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Meghana, Rasamalla. "Universal Score-based Speech Enhancement with High Content Preservation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49349.

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Abstract - Speech enhancement aims to improve the quality and intelligibility of speech signals corrupted by noise or distortions. Traditional methods often struggle to generalize across diverse noise types and acoustic conditions, limiting their real-world applicability. In this work, we propose a universal score-based speech enhancement framework that leverages recent advances in score-based generative modeling to robustly denoise speech signals while preserving critical speech content. Our approach models the complex speech distribution through a learned score function, enabling effective r

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Li, Dengshi, Chenyi Zhu, and Lanxin Zhao. "D2StarGAN: A Near-Far End Noise Adaptive StarGAN for Speech Intelligibility Enhancement." Electronics 12, no. 17 (2023): 3620. http://dx.doi.org/10.3390/electronics12173620.

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When using mobile communication, the voice output from the device is already relatively clear, but in a noisy environment, it is difficult for the listener to obtain the information expressed by the speaker with clarity. Consequently, speech intelligibility enhancement technology has emerged to help alleviate this problem. Speech intelligibility enhancement (IENH) is a technique that enhances speech intelligibility during the reception phase. Previous research has focused on IENH through normal versus different levels of Lombardic speech conversion, inspired by a well-known acoustic mechanism

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Goldberg, Hyman. "Electroacoustic speech intelligibility enhancement method and apparatus." Journal of the Acoustical Society of America 101, no. 3 (1997): 1221. http://dx.doi.org/10.1121/1.419429.

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Srinivasarao, V., and Umesh Ghanekar. "Speech intelligibility enhancement: a hybrid wiener approach." International Journal of Speech Technology 23, no. 3 (2020): 517–25. http://dx.doi.org/10.1007/s10772-020-09737-4.

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Sahu, Smriti, and Neela Rayavarapu. "Compressive speech enhancement using semi-soft thresholding and improved threshold estimation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 2788. http://dx.doi.org/10.11591/ijece.v13i3.pp2788-2800.

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<span lang="EN-US">Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basis-function based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-

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Lohith, Lakkakula. "Speech Enhancement via Metric GAN and Kolmogorov-Arnold Networks: A Deep Learning Approach in Python." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49208.

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Abstract - Speech enhancement in noisy environments remains a critical challenge for robust voice communication systems. Traditional signal processing techniques and supervised deep learning models often struggle to generalize to diverse noise conditions and fail to optimize for human perceptual quality. This paper proposes a novel Metric GAN+KAN architecture, which integrates a Generative Adversarial Network (GAN) with Kolmogorov-Arnold Networks (KAN) to enhance speech signals by focusing both on perceptual fidelity and structural consistency. The GAN-based generator learns to map noisy speec

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Espy-Wilson, Carol Y., Venkatesh R. Chari, Joel M. MacAuslan, Caroline B. Huang, and Michael J. Walsh. "Enhancement of Electrolaryngeal Speech by Adaptive Filtering." Journal of Speech, Language, and Hearing Research 41, no. 6 (1998): 1253–64. http://dx.doi.org/10.1044/jslhr.4106.1253.

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Artificial larynges provide a means of verbal communication for people who have either lost or are otherwise unable to use their larynges. Although they enable adequate communication, the resulting speech has an unnatural quality and is significantly less intelligible than normal speech. One of the major problems with the widely used Transcutaneous Artificial Larynx (TAL) is the presence of a steady background noise caused by the leakage of acoustic energy from the TAL, its interface with the neck, and the surrounding neck tissue. The severity of the problem varies from speaker to speaker, par

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Veeramakal, T., Syed Raffi Ahamed J, and Bagiyalakshmi N. "Speech Signal Enhancement with Integrated Weighted Filtering for PSNR Reduction in Multimedia Applications." Journal of Computer Allied Intelligence 2, no. 3 (2024): 1–14. http://dx.doi.org/10.69996/jcai.2024011.

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This paper investigates the effectiveness of the Weighted Kalman Integrated Band Rejection (WKBR) method for enhancing speech signals in multimedia applications. Speech enhancement is crucial for improving the quality and intelligibility of audio in environments with varying noise types and levels. The WKBR method is evaluated across ten different noise scenarios, including white noise, babble noise, street noise, airplane cabin noise, and more. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Short-Time Objective Intelligibility (STOI) are used to q

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Guan, Jingjing, and Chang Liu. "Speech Perception in Noise With Formant Enhancement for Older Listeners." Journal of Speech, Language, and Hearing Research 62, no. 9 (2019): 3290–301. http://dx.doi.org/10.1044/2019_jslhr-s-18-0089.

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Purpose Degraded speech intelligibility in background noise is a common complaint of listeners with hearing loss. The purpose of the current study is to explore whether 2nd formant (F2) enhancement improves speech perception in noise for older listeners with hearing impairment (HI) and normal hearing (NH). Method Target words (e.g., color and digit) were selected and presented based on the paradigm of the coordinate response measure corpus. Speech recognition thresholds with original and F2-enhanced speech in 2- and 6-talker babble were examined for older listeners with NH and HI. Results The

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Kates, James M. "Speech Enhancement Based on a Sinusoidal Model." Journal of Speech, Language, and Hearing Research 37, no. 2 (1994): 449–64. http://dx.doi.org/10.1044/jshr.3702.449.

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Sinusoidal modeling is a new procedure for representing the speech signal. In this approach, the signal is divided into overlapping segments, the Fourier transform computed for each segment, and a set of desired spectral peaks is identified. The speech is then resynthesized using sinusoids that have the frequency, amplitude, and phase of the selected peaks, with the remaining spectral information being discarded. Using a limited number of sinusoids to reproduce speech in a background of multi-talker speech babble results in a speech signal that has an improved signal-to-noise ratio and enhance

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Graetzer, Simone, and Carl Hopkins. "Comparison of ideal mask-based speech enhancement algorithms for speech mixed with white noise at low mixture signal-to-noise ratios." Journal of the Acoustical Society of America 152, no. 6 (2022): 3458–70. http://dx.doi.org/10.1121/10.0016494.

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The literature shows that the intelligibility of noisy speech can be improved by applying an ideal binary or soft gain mask in the time-frequency domain for signal-to-noise ratios (SNRs) between –10 and +10 dB. In this study, two mask-based algorithms are compared when applied to speech mixed with white Gaussian noise (WGN) at lower SNRs, that is, SNRs from −29 to –5 dB. These comprise an Ideal Binary Mask (IBM) with a Local Criterion (LC) set to 0 dB and an Ideal Ratio Mask (IRM). The performance of three intrusive Short-Time Objective Intelligibility (STOI) variants—STOI, STOI+, and Extended

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Ullah, Rizwan, Lunchakorn Wuttisittikulkij, Sushank Chaudhary, et al. "End-to-End Deep Convolutional Recurrent Models for Noise Robust Waveform Speech Enhancement." Sensors 22, no. 20 (2022): 7782. http://dx.doi.org/10.3390/s22207782.

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Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we pre

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Gopi Tilak, V., and S. Koteswara Rao. "Dual and joint estimation for speech enhancement." International Journal of Engineering & Technology 7, no. 2.7 (2018): 5. http://dx.doi.org/10.14419/ijet.v7i2.7.10243.

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Maintaining good quality and intelligibility of speech is the primary constraint in mobile communications. The present work is on the enhancement of speech under the consideration of additive white and colored noise environments using Kalman filter. Dual and Joint estimation techniques were applied and the quality of speech is analyzed through the signal to noise ratio. The techniques were applied in both ideal and practical cases for two different speech samples.

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Y, Sravanthi. "LSTM - Aided Speech Enhancement with Wiener Filter Adaptation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30882.

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Speech enhancement plays a pivotal role in various applications, from improving the intelligibility of spoken communication in noisy environments. With the assistance of deep learning, a novel approach speech signal enhancement model is introduced in this research. The proposed LSTM model estimates the tuning factor of the Wiener filter with the aid of extracted features to obtain the de-noised speech signal. This model is structured into two phases: Training and Testing. During the training phase, Non-negative Matrix Factorization (NMF) is employed to estimate both the noise and signal spectr

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Saunders, Gabrielle H., and James M. Kates. "Speech intelligibility enhancement using hearing-aid array processing." Journal of the Acoustical Society of America 102, no. 3 (1997): 1827–37. http://dx.doi.org/10.1121/1.420107.

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Hong, Sae Mi, and Hyun Sub Sim. "Planning of Speech Intelligibility Enhancement Program for Dysarthria." AAC Research & Practice 3, no. 2 (2015): 177. http://dx.doi.org/10.14818/aac.2015.12.3.2.177.

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Abajaddi, Nesrine, Youssef Elfahm, Badia Mounir, and Abdelmajid Farchi. "A robust speech enhancement method in noisy environments." International journal of electrical and computer engineering systems 14, no. 9 (2023): 973–83. http://dx.doi.org/10.32985/ijeces.14.9.2.

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Speech enhancement aims to eliminate or reduce undesirable noises and distortions, this processing should keep features of the speech to enhance the quality and intelligibility of degraded speech signals. In this study, we investigated a combined approach using single-frequency filtering (SFF) and a modified spectral subtraction method to enhance single-channel speech. The SFF method involves dividing the speech signal into uniform subband envelopes, and then performing spectral over-subtraction on each envelope. A smoothing parameter, determined by the a-posteriori signal-to-noise ratio (SNR)

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Blake, Helen L. "Intelligibility Enhancement via Telepractice During COVID-19 Restrictions." Perspectives of the ASHA Special Interest Groups 5, no. 6 (2020): 1797–800. http://dx.doi.org/10.1044/2020_persp-20-00133.

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Purpose Speech-language pathologists (SLPs) may be approached by multilingual speakers seeking to improve their intelligibility in English. Intelligibility is an essential element of spoken language proficiency and is especially important for multilingual university students given their need to express complex ideas in an additional language. Intelligibility Enhancement is an assessment and intervention approach that aims to improve the intelligibility of consonants, vowels, and prosody with multilingual speakers who are learning to speak English. This article describes the student-led deliver

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Fallah, Ali, and Steven van de Par. "A Speech Preprocessing Method Based on Perceptually Optimized Envelope Processing to Increase Intelligibility in Reverberant Environments." Applied Sciences 11, no. 22 (2021): 10788. http://dx.doi.org/10.3390/app112210788.

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Speech intelligibility in public places can be degraded by the environmental noise and reverberation. In this study, a new near-end listening enhancement (NELE) approach is proposed in which using a time varying filter jointly enhances the onsets and reduces the overlap masking. For optimization, some look-ahead in clean speech and prior knowledge of room impulse response (RIR) are required. In this method, by optimizing a defined cost function, the Spectro-Temporal Envelope of reverb speech is optimized to be as close as possible to that of clean speech. In this cost function, onsets of speec

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Park, Gyuseok, Woohyeong Cho, Kyu-Sung Kim, and Sangmin Lee. "Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises." Applied Sciences 10, no. 17 (2020): 6077. http://dx.doi.org/10.3390/app10176077.

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Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classificati

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Van Engen, Kristin J., Jasmine E. B. Phelps, Rajka Smiljanic, and Bharath Chandrasekaran. "Enhancing Speech Intelligibility: Interactions Among Context, Modality, Speech Style, and Masker." Journal of Speech, Language, and Hearing Research 57, no. 5 (2014): 1908–18. http://dx.doi.org/10.1044/jslhr-h-13-0076.

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Purpose The authors sought to investigate interactions among intelligibility-enhancing speech cues (i.e., semantic context, clearly produced speech, and visual information) across a range of masking conditions. Method Sentence recognition in noise was assessed for 29 normal-hearing listeners. Testing included semantically normal and anomalous sentences, conversational and clear speaking styles, auditory-only (AO) and audiovisual (AV) presentation modalities, and 4 different maskers (2-talker babble, 4-talker babble, 8-talker babble, and speech-shaped noise). Results Semantic context, clear spe

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Cox, Trevor, Michael Akeroyd, Jon Barker, et al. "Predicting Speech Intelligibility for People with a Hearing Loss: The Clarity Challenges." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 3 (2023): 4599–606. http://dx.doi.org/10.3397/in_2022_0662.

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Objective speech intelligibility metrics are used to reduce the need for time consuming listening tests. They are used in the design of audio systems; room acoustics and signal processing algorithms. Most published speech intelligibility metrics have been developed using young adults with so-called 'normal hearing', and therefore do not work well for those with different hearing characteristics. One of the most common causes of aural diversity is sensorineural hearing loss. While partially restoring perception through hearing aids is possible, results are mixed. This has led to the Clarity Pro

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Jiang, Yi, Hong Zhou, Yuan Yuan Zu, and Xiao Chen. "Energy Based Dual-Microphone Electronic Speech Segregation." Applied Mechanics and Materials 385-386 (August 2013): 1381–84. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1381.

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Speech segregation based on energy has a good performance on dual-microphone electronic speech signal processing. The implication of the binary mask to an auditory mixture has been shown to yield substantial improvements in signal-to-noise-ratio (SNR) and intelligibility. To evaluate the performance of a binary mask based dual microphone speech enhancement algorithm, various spatial noise sources and reverberation test conditions are used. Two compare dual microphone systems based on energy difference and machine learning are used at the same time. Result with SNR and speech intelligibility sh

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Snyder, Gregory J., Molly Grace Williams, Molly E. Gough, and Paul G. Blanchet. "Fluency-Enhancing Strategies for Hypokinetic Dysarthria Exacerbated by Subthalamic Nucleus Brain Stimulation: A Case Study." Perspectives of the ASHA Special Interest Groups 3, no. 4 (2018): 4–16. http://dx.doi.org/10.1044/persp3.sig4.4.

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Introduction Speech disorders associated with Parkinson's disease (PD) and the pharmaceutical treatments of PD are well documented. A relatively recent treatment alternative for PD is deep brain stimulation (DBS) of the subthalamic nucleus (STN), which is used to manage the symptoms of PD as the disease progresses. This case study documented the speech characteristics of a unique client with PD STN-DBS and reported initial findings on a variety of fluency- and intelligibility-enhancing strategies. Method A speech-language pathologist referred a 63-year-old man, previously diagnosed by a speech

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Li, Qiuying, Tao Zhang, Yanzhang Geng, and Zhen Gao. "Microphone array speech enhancement based on optimized IMCRA." Noise Control Engineering Journal 69, no. 6 (2021): 468–76. http://dx.doi.org/10.3397/1/376944.

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Microphone array speech enhancement algorithm uses temporal and spatial informa- tion to improve the performance of speech noise reduction significantly. By combining noise estimation algorithm with microphone array speech enhancement, the accuracy of noise estimation is improved, and the computation is reduced. In traditional noise es- timation algorithms, the noise power spectrum is not updated in the presence of speech, which leads to the delay and deviation of noise spectrum estimation. An optimized im- proved minimum controlled recursion average speech enhancement algorithm, based on a mi

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Md. Easir Arafat, Indraneel Misra, and Md. Ekramul Hamid. "A comparative study for throat microphone speech enhancement with different approaches." International Journal of Science and Research Archive 13, no. 1 (2024): 850–59. http://dx.doi.org/10.30574/ijsra.2024.13.1.1631.

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Throat microphones (TM) offer significant advantages in noisy environments by capturing speech signals directly from the throat, thus minimizing external noise. However, TM signals often lack clarity and intelligibility compared to conventional microphones. This paper presents a comparative study of three prominent feature extraction techniques—Mel-frequency cepstral coefficients (MFCC), Linear Predictive Cepstral coefficients (LPCC), Perceptual Linear Prediction (PLP) for enhancing speech captured by throat microphones. Each technique is evaluated based on its ability to enhance speech clarit

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Brochier, Tim J., Amanda Fullerton, Adam Hersbach, Harish Krishnamoorthi, and Zachary Smith. "Deep neural network-based speech enhancement for cochlear implants." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A28. http://dx.doi.org/10.1121/10.0022678.

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Noisy conditions make understanding speech with a cochlear implant (CI) difficult. Speech enhancement (SE) algorithms based on signal statistics can be beneficial in stationary noise, but rarely provide benefit in modulated multi-talker babble. Current approaches using deep neural networks (DNNs) rely on a data driven approach for training and promise improvements in a wide variety of noisy conditions. In this study a DNN-based SE algorithm was evaluated in CI listeners. The network was trained on a large database of publicly available recordings. A double-blinded acute evaluation was conducte

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Thoidis, Iordanis, Lazaros Vrysis, Dimitrios Markou, and George Papanikolaou. "Temporal Auditory Coding Features for Causal Speech Enhancement." Electronics 9, no. 10 (2020): 1698. http://dx.doi.org/10.3390/electronics9101698.

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Perceptually motivated audio signal processing and feature extraction have played a key role in the determination of high-level semantic processes and the development of emerging systems and applications, such as mobile phone telecommunication and hearing aids. In the era of deep learning, speech enhancement methods based on neural networks have seen great success, mainly operating on the log-power spectra. Although these approaches surpass the need for exhaustive feature extraction and selection, it is still unclear whether they target the important sound characteristics related to speech per

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Takale, Dattatray G., Shreyas Thombal, Najim Tadvi, Sunil Sonu, Samadhan Suryawanashi, and Ashwajit Surwade. "Speech Enhancement Using Machine Learning." Journal of Electrical Engineering and Electronics Design 2, no. 1 (2024): 11–15. http://dx.doi.org/10.48001/joeeed.2024.2111-15.

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The incorporation of machine learning, specifically deep learning, into speech enhancement algorithms represents an advanced methodology aimed at restoring original speech signals from distorted counterparts. This innovative approach incorporates the use of Charlier polynomials-based discrete transform, particularly the discrete Charlier transform (DCHT), to extract spectra from noisy signals employing a fully connected neural network. Leveraging the capabilities of deep learning, particularly in handling nonlinear mapping challenges, the system acquires contextual information from speech sign

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Chilakawad, Aparna, and Pandurangarao N. Kulkarni. "Spectral splitting of speech signal using time varying recursive filters for binaural hearing aids." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4998. http://dx.doi.org/10.11591/ijai.v13.i4.pp4998-5004.

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<p>Speech perception in noisy environments is reduced in human with sensorineural hearing loss (SNHL) due to masking. Moderate SNHL cannot be cured medically hence masking effects should be reduced to enhance speech perception. To reduce masking, processing delay and hardware complexity the present paper is proposed a scheme to partition the voice signal into two signals which are complementary to each other by using the filter-bank summation method (FBSM) with a set of time-varying recursive band pass filters. Performance of the filter is evaluated with following measures: perceptual ev

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Aparna, Chilakawad, and N. Kulkarni Pandurangarao. "Spectral splitting of speech signal using time varying recursive filters for binaural hearing aids." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4998–5004. https://doi.org/10.11591/ijai.v13.i4.pp4998-5004.

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Abstract:

Speech perception in noisy environments is reduced in human with sensorineural hearing loss (SNHL) due to masking. Moderate SNHL cannot be cured medically hence masking effects should be reduced to enhance speech perception. To reduce masking, processing delay and hardware complexity the present paper is proposed a scheme to partition the voice signal into two signals which are complementary to each other by using the filter-bank summation method (FBSM) with a set of time-varying recursive band pass filters. Performance of the filter is evaluated with following measures: perceptual evaluation

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Munson, Benjamin. "Audiovisual enhancement and single-word intelligibility in children's speech." Journal of the Acoustical Society of America 148, no. 4 (2020): 2765. http://dx.doi.org/10.1121/1.5147696.

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Harris, John G., and Mark D. Skowronski. "Energy redistribution speech intelligibility enhancement, vocalic and transitional cues." Journal of the Acoustical Society of America 112, no. 5 (2002): 2305. http://dx.doi.org/10.1121/1.4808562.

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