Prediction of speech recognition in background noise and competing speech from suprathreshold auditory and cognitive measures

2021 ◽  
Vol 150 (4) ◽  
pp. A305-A305
Author(s):  
Jonathan H. Venezia ◽  
Nicole Whittle ◽  
Christian Herrera Ortiz ◽  
Marjorie R. Leek ◽  
Caleb Barcenas ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Background Noise ◽  
Cognitive Measures ◽  
Competing Speech

Effects of word frequency and phonological neighborhood density on speech recognition in background noise and competing speech

2021 ◽  
Vol 150 (4) ◽  
pp. A276-A276
Author(s):  
Nicole Whittle ◽  
Christian Herrera Ortiz ◽  
Marjorie R. Leek ◽  
Jerome Heidrich ◽  
Mark Jenkins ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Word Frequency ◽  
Background Noise ◽  
Neighborhood Density ◽  
Phonological Neighborhood Density ◽  
Phonological Neighborhood ◽  
Competing Speech

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

scholarly journals Low background noise increases cognitive load in older adults listening to competing speech

2018 ◽  
Vol 144 (5) ◽  
pp. EL417-EL422 ◽  
Author(s):  
Hartmut Meister ◽  
Sebastian Rählmann ◽  
Martin Walger
Keyword(s):  
Older Adults ◽  
Cognitive Load ◽  
Background Noise ◽  
Low Background ◽  
Competing Speech

Robust Feature Vector Set Using Higher Order Autocorrelation Coefficients

2012 ◽  
pp. 126-134
Author(s):  
Poonam Bansal ◽  
Amita Dev ◽  
Shail Jain
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Background Noise ◽  
Extraction Method ◽  
Recognition Performance ◽  
Spectral Estimation ◽  
Higher Order ◽  
Feature Extraction Method ◽  
Power Spectral ◽  
Cepstral Coefficients

In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower orders, while the higher-order autocorrelation coefficients are least affected, this method discards the lower order autocorrelation coefficients and uses only the higher-order autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-order autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further by the Mel filter bank; a log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the Differentiated Relative Higher Order Autocorrelation Coefficient Sequence Spectrum (DRHOASS). The authors evaluate the speech recognition performance of the DRHOASS features and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech.

scholarly journals Tinnitus impairs segregation of competing speech in normal-hearing listeners

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yang Wenyi Liu ◽  
Bing Wang ◽  
Bing Chen ◽  
John J. Galvin ◽  
Qian-Jie Fu
Keyword(s):  
Sex Differences ◽  
Speech Recognition ◽  
Auditory Processing ◽  
Normal Hearing ◽  
Central Auditory Processing ◽  
Informational Masking ◽  
Spatial Cues ◽  
Processing Resources ◽  
Lexical Interference ◽  
Competing Speech

AbstractMany tinnitus patients report difficulties understanding speech in noise or competing talkers, despite having “normal” hearing in terms of audiometric thresholds. The interference caused by tinnitus is more likely central in origin. Release from informational masking (more central in origin) produced by competing speech may further illuminate central interference due to tinnitus. In the present study, masked speech understanding was measured in normal hearing listeners with or without tinnitus. Speech recognition thresholds were measured for target speech in the presence of multi-talker babble or competing speech. For competing speech, speech recognition thresholds were measured for different cue conditions (i.e., with and without target-masker sex differences and/or with and without spatial cues). The present data suggest that tinnitus negatively affected masked speech recognition even in individuals with no measurable hearing loss. Tinnitus severity appeared to especially limit listeners’ ability to segregate competing speech using talker sex differences. The data suggest that increased informational masking via lexical interference may tax tinnitus patients’ central auditory processing resources.

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