scholarly journals Automatic Parameter Switching of Noise Reduction for Speech Recognition

2017 ◽  
Vol 21 (2) ◽  
pp. 63-71 ◽  
Author(s):  
Tomoko Kawase ◽  
Kenta Niwa ◽  
Yusuke Hioka ◽  
Kazunori Kobayashi
Keyword(s):  
Speech Recognition ◽  
Noise Reduction ◽  
Parameter Switching

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.

scholarly journals Comparative Evaluation of Single-Channel MMSE-Based Noise Reduction Schemes for Speech Recognition

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Emanuele Principi ◽  
Simone Cifani ◽  
Rudy Rotili ◽  
Stefano Squartini ◽  
Francesco Piazza
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Noise Reduction ◽  
Automatic Speech Recognition ◽  
Comparative Evaluation ◽  
Scientific Community ◽  
Additive Noise ◽  
Single Channel ◽  
Acoustic Conditions

One of the big challenges in the field of Automatic Speech Recognition (ASR) consists in developing suitable solutions able to work properly also in adverse acoustic conditions, like in presence of additive noise and/or in reverberant rooms. Recently a certain attention has been paid to deeply integrate the noise suppressor in the feature extraction pipeline. In this paper, different single-channel MMSE-based noise reduction schemes have been implemented both in the frequency and cepstral domains and the related recognition performances evaluated on the AURORA2 and AURORA4 databases, therefore providing a useful reference for the scientific community.

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
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