scholarly journals Dual-Channel Speech Enhancement Based on Speech Presence Probability

2020 ◽  
Vol 9 (3) ◽  
pp. 2198-2200
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Speech Processing ◽  
Speech Signal ◽  
Reduction Process ◽  
Coherent Noise ◽  
Residual Noise ◽  
Speech Distortion ◽  
Reduction Methods

This paper introduces technology to improve sound quality, which serves the needs of media and entertainment. Major challenging problem in the speech processing applications like mobile phones, hands-free phones, car communication, teleconference systems, hearing aids, voice coders, automatic speech recognition and forensics etc., is to eliminate the background noise. Speech enhancement algorithms are widely used for these applications in order to remove the noise from degraded speech in the noisy environment. Hence, the conventional noise reduction methods introduce more residual noise and speech distortion. So, it has been found that the noise reduction process is more effective to improve the speech quality but it affects the intelligibility of the clean speech signal. In this paper, we introduce a new model of coherence-based noise reduction method for the complex noise environment in which a target speech coexists with a coherent noise around. From the coherence model, the information of speech presence probability is added to better track noise variation accurately; and during the speech presence and speech absent period, adaptive coherence-based method is adjusted. The performance of suggested method is evaluated in condition of diffuse and real street noise, and it improves the speech signal quality less speech distortion and residual noise.

scholarly journals Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises

2020 ◽  
Vol 10 (17) ◽  
pp. 6077
Author(s):  
Gyuseok Park ◽  
Woohyeong Cho ◽  
Kyu-Sung Kim ◽  
Sangmin Lee
Keyword(s):  
Neural Networks ◽  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Deep Neural Networks ◽  
Hearing Aid ◽  
Environmental Noise ◽  
Speech Quality ◽  
Impaired Hearing ◽  
Noise Classification

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 classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.

Application of the Speech Enhancement Technology in Flight Simulation System

2014 ◽  
Vol 556-562 ◽  
pp. 3408-3411
Author(s):  
Jing Xin Xiao ◽  
Lin Deng ◽  
Yun He ◽  
You Yi Li
Keyword(s):  
Speech Enhancement ◽  
Speech Processing ◽  
Speech Signal ◽  
Research Team ◽  
Simulation System ◽  
Flight Simulation ◽  
Spectral Subtraction ◽  
Enhancement Technology ◽  
Interrupted Speech

Speech signal can be effect by the external interruption in the process of flight simulation, which results in the deteriorated speech processing performance in flight simulation system. To solve the problem, speech enhancement module has been developed using the spectral subtraction algorithm based on the flight simulation system developed by the research team. The module will improve the recognition rates and provide advanced anti-jam capabilities for the speech processing performance of the flight simulation system. The feasibility and validity of this speech enhancement module is validated by processing the interrupted speech signal. The work in this paper has laid foundation for the application of the speech enhancement technology in flight simulation system.

Using the Repeat-Recall Test to Examine Factors Affecting Context Use

2021 ◽  
Author(s):  
Francis Kuk ◽  
Christopher Slugocki ◽  
Petri Korhonen
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Speech Processing ◽  
Repeated Measures ◽  
Recall Test ◽  
Outcome Measure ◽  
Hearing Aid ◽  
Listening Effort ◽  
Double Blind ◽  
Repeated Measures Design

Abstract Background The effect of context on speech processing has been studied using different speech materials and response criteria. The Repeat-Recall Test (RRT) evaluates listener performance using high context (HC) and low context (LC) sentences; this may offer another platform for studying context use (CU). Objective This article aims to evaluate if the RRT may be used to study how different signal-to-noise ratios (SNRs), hearing aid technologies (directional microphone and noise reduction), and listener working memory capacities (WMCs) interact to affect CU on the different measures of the RRT. Design Double-blind, within-subject repeated measures design. Study Sample Nineteen listeners with a mild-to-moderately severe hearing loss. Data Collection The RRT was administered with participants wearing the study hearing aids under two microphone (omnidirectional vs. directional) by two noise reduction (on vs. off) conditions. Speech was presented from 0 degree at 75 dB sound pressure level and a continuous speech-shaped noise from 180 degrees at SNRs of 0, 5, 10, and 15 dB. The order of SNR and hearing aid conditions was counterbalanced across listeners. Each test condition was completed twice in two 2-hour sessions separated by 1 month. Results CU was calculated as the difference between HC and LC sentence scores for each outcome measure (i.e., repeat, recall, listening effort, and tolerable time). For all outcome measures, repeated measures analyses of variance revealed that CU was significantly affected by the SNR of the test conditions. For repeat, recall, and listening effort measures, these effects were qualified by significant two-way interactions between SNR and microphone mode. In addition, the WMC group significantly affected CU during recall and rating of listening effort, the latter of which was qualified by an interaction between the WMC group and SNR. Listener WMC affected CU on estimates of tolerable time as qualified by significant two-way interactions between SNR and microphone mode. Conclusion The study supports use of the RRT as a tool for measuring how listeners use sentence context to aid in speech processing. The degree to which context influenced scores on each outcome measure of the RRT was found to depend on complex interactions between the SNR of the listening environment, hearing aid features, and the WMC of the listeners.

scholarly journals Noise Reduction in Car Speech

10.14311/1111 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
V. Bolom
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Mixed Model ◽  
Noise Suppression ◽  
Speech Signals ◽  
Noisy Environment ◽  
Free Communication ◽  
Speech Distortion ◽  
Criteria For Evaluation

This paper presents properties of chosen multichannel algorithms for speech enhancement in a noisy environment. These methods are suitable for hands-free communication in a car cabin. Criteria for evaluation of these systems are also presented. The criteria consider both the level of noise suppression and the level of speech distortion. The performance of multichannel algorithms is investigated for a mixed model of speech signals and car noise and for real signals recorded in a car. 

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