Waveform-based speech recognition using hidden filter models: parameter selection and sensitivity to power normalization

1994 ◽  
Vol 2 (1) ◽  
pp. 80-89 ◽  
Author(s):  
H. Sheikhzadeh ◽  
L. Deng
Keyword(s):  
Speech Recognition ◽  
Parameter Selection ◽  
Power Normalization

Parameter selection to optimize speech recognition with the Nucleus implant

1997 ◽  
Vol 117 (3) ◽  
pp. 188-195 ◽  
Author(s):  
M SKINNER ◽  
L HOLDEN ◽  
T HOLDEN
Keyword(s):  
Speech Recognition ◽  
Parameter Selection

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.

Effects of Aging on Response Criteria in Speech-Recognition Tasks

1986 ◽  
Vol 29 (2) ◽  
pp. 155-162 ◽  
Author(s):  
Sandra Gordon-Salant
Keyword(s):  
Speech Recognition ◽  
Response Criteria ◽  
Effects Of Aging

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 ◽  
Free Data

Using Your Voice: Speech Recognition Technology in Medicine and Surgery

1986 ◽  
Vol 13 (3) ◽  
pp. 509-511 ◽  
Author(s):  
Glenn A. Akers
Keyword(s):  
Speech Recognition ◽  
Speech Recognition Technology

Use of kohonen self-organising feature maps for HMM parameter smoothing in speech recognition

1992 ◽  
Vol 139 (6) ◽  
pp. 385 ◽  
Author(s):  
Z. Zhao ◽  
C.G. Rowden
Keyword(s):  
Speech Recognition ◽  
Feature Maps
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