Speech Recognition Over Mobile Networks

2008 ◽  
pp. 41-61
Author(s):  
Hong Kook Kim ◽  
Richard C. Rose
Keyword(s):  
Speech Recognition ◽  
Mobile Networks

Acoustic model training for speech recognition over mobile networks

2013 ◽  
Vol 6 (2) ◽  
pp. 65
Author(s):  
Juraj Vojtko ◽  
Juraj Kačur ◽  
Gregor Rozinaj ◽  
Ján Kőrösi
Keyword(s):  
Speech Recognition ◽  
Mobile Networks ◽  
Acoustic Model ◽  
Model Training

Measuring Mandarin Speech Recognition Thresholds Using the Method of Adaptive Tracking

2019 ◽  
Vol 62 (6) ◽  
pp. 2009-2017
Author(s):  
Yuxia Wang ◽  
Zhaoyu Lu ◽  
Xiaohu Yang ◽  
Chang Liu
Keyword(s):  
Speech Recognition ◽  
Adaptive Tracking ◽  
Mandarin Speech Recognition

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.

Aided Speech Recognition Abilities of Adults With a Severe or Severe-to-Profound Hearing Loss

1998 ◽  
Vol 41 (2) ◽  
pp. 285-299 ◽  
Author(s):  
Mark C. Flynn ◽  
Richard C. Dowell ◽  
Graeme M. Clark
Keyword(s):  
Hearing Loss ◽  
Speech Recognition ◽  
Profound Hearing Loss

Using an Avatar, RFID & speech recognition for hands-free data collection

2008 ◽  
Author(s):  
Kristie Nemeth ◽  
Nicole Arbuckle ◽  
Andrea Snead ◽  
Drew Bowers ◽  
Christopher Burneka ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Data Collection ◽  
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