Analysis of Noise Reduction Techniques in Speech Recognition

2020 ◽  
Author(s):  
Bo Zheng ◽  
Jinsong Hu ◽  
Ge Zhang ◽  
Yuling Wu ◽  
Jianshuang Deng
Keyword(s):  
Speech Recognition ◽  
Noise Reduction ◽  
Reduction Techniques

Multi-microphone noise reduction techniques as front-end devices for speech recognition

2001 ◽  
Vol 34 (1-2) ◽  
pp. 3-12 ◽  
Author(s):  
Joerg Bitzer ◽  
Klaus Uwe Simmer ◽  
Karl-Dirk Kammeyer
Keyword(s):  
Speech Recognition ◽  
Noise Reduction ◽  
Reduction Techniques ◽  
Front End

scholarly journals An ensemble technique for speech recognition in noisy environments

2020 ◽  
Vol 18 (2) ◽  
pp. 835
Author(s):  
Imad Qasim Habeeb ◽  
Tamara Z. Fadhil ◽  
Yaseen Naser Jurn ◽  
Zeyad Qasim Habeeb ◽  
Hanan Najm Abdulkhudhur
Keyword(s):  
Speech Recognition ◽  
Noise Reduction ◽  
Speech Signal ◽  
Background Noise ◽  
Test Results ◽  
Noisy Environments ◽  
Ensemble Technique ◽  
Reduction Techniques ◽  
Speech Features ◽  
Main Factor

<span>Automatic speech recognition (ASR) is a technology that allows a computer and mobile device to recognize and translate spoken language into text. ASR systems often produce poor accuracy for the noisy speech signal. Therefore, this research proposed an ensemble technique that does not rely on a single filter for perfect noise reduction but incorporates information from multiple noise reduction filters to improve the final ASR accuracy. The main factor of this technique is the generation of K-copies of the speech signal using three noise reduction filters. The speech features of these copies differ slightly in order to extract different texts from them when processed by the ASR system. Thus, the best among these texts can be elected as final ASR output. The ensemble technique was compared with three related current noise reduction techniques in terms of CER and WER. The test results were encouraging and showed a relatively decreased by 16.61% and 11.54% on CER and WER compared with the best current technique. ASR field will benefit from the contribution of this research to increase the recognition accuracy of a human speech in the presence of background noise.</span>

scholarly journals Feasibility of Total Variation Noise Reduction Algorithm According to Various MR-Based PET Images in a Simultaneous PET/MR System: A Phantom Study

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 319
Author(s):  
Chan-Rok Park ◽  
Seong-Hyeon Kang ◽  
Young-Jin Lee
Keyword(s):  
Image Quality ◽  
Noise Reduction ◽  
Total Variation ◽  
Parallel Imaging ◽  
Pulse Sequence ◽  
Reduction Algorithm ◽  
Median Filters ◽  
Reduction Techniques ◽  
Imaging Results ◽  
Noise Reduction Algorithm

Recently, the total variation (TV) algorithm has been used for noise reduction distribution in degraded nuclear medicine images. To acquire positron emission tomography (PET) to correct the attenuation region in the PET/magnetic resonance (MR) system, the MR Dixon pulse sequence, which is based on controlled aliasing in parallel imaging, results from higher acceleration (CAIPI; MR-ACDixon-CAIPI) and generalized autocalibrating partially parallel acquisition (GRAPPA; MR-ACDixon-GRAPPA) algorithms are used. Therefore, this study aimed to evaluate the image performance of the TV noise reduction algorithm for PET/MR images using the Jaszczak phantom by injecting 18F radioisotopes with PET/MR, which is called mMR (Siemens, Germany), compared with conventional noise-reduction techniques such as Wiener and median filters. The contrast-to-noise (CNR) and coefficient of variation (COV) were used for quantitative analysis. Based on the results, PET images with the TV algorithm were improved by approximately 7.6% for CNR and decreased by approximately 20.0% for COV compared with conventional noise-reduction techniques. In particular, the image quality for the MR-ACDixon-CAIPI PET image was better than that of the MR-ACDixon-GRAPPA PET image. In conclusion, the TV noise-reduction algorithm is efficient for improving the PET image quality in PET/MR systems.

scholarly journals Methods for Clinical Evaluation of Noise Reduction Techniques in Abdominopelvic CT

Radiographics ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 849-862 ◽  
Author(s):  
Eric C. Ehman ◽  
Lifeng Yu ◽  
Armando Manduca ◽  
Amy K. Hara ◽  
Maria M. Shiung ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Clinical Evaluation ◽  
Reduction Techniques ◽  
Abdominopelvic Ct

scholarly journals Multitasking with typical use of hearing aid noise reduction in older listeners

2018 ◽  
Author(s):  
Tim Schoof ◽  
Pamela Souza
Keyword(s):  
Speech Recognition ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Recognition Task ◽  
Hearing Impaired ◽  
Improve Performance ◽  
Sentence Recognition ◽  
Monitoring Task ◽  
Speech In Noise ◽  
Dual Task Paradigm

Objective: Older hearing-impaired adults typically experience difficulties understanding speech in noise. Most hearing aids address this issue using digital noise reduction. While noise reduction does not necessarily improve speech recognition, it may reduce the resources required to process the speech signal. Those available resources may, in turn, aid the ability to perform another task while listening to speech (i.e., multitasking). This study examined to what extent changing the strength of digital noise reduction in hearing aids affects the ability to multitask. Design: Multitasking was measured using a dual-task paradigm, combining a speech recognition task and a visual monitoring task. The speech recognition task involved sentence recognition in the presence of six-talker babble at signal-to-noise ratios (SNRs) of 2 and 7 dB. Participants were fit with commercially-available hearing aids programmed under three noise reduction settings: off, mild, strong. Study sample: 18 hearing-impaired older adults. Results: There were no effects of noise reduction on the ability to multitask, or on the ability to recognize speech in noise. Conclusions: Adjustment of noise reduction settings in the clinic may not invariably improve performance for some tasks.

Sign in / Sign up

Export Citation Format

Share Document