Linear trajectory models incorporating preprocessing parameters for speech recognition

R. Chengalvarayan

doi:10.1109/97.661563

Linear trajectory models incorporating preprocessing parameters for speech recognition

IEEE Signal Processing Letters ◽

10.1109/97.661563 ◽

1998 ◽

Vol 5 (3) ◽

pp. 66-68 ◽

Cited By ~ 3

Author(s):

R. Chengalvarayan

Keyword(s):

Speech Recognition ◽

Linear Trajectory ◽

Trajectory Models

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A theoretical analysis of speech recognition based on feature trajectory models

10.21437/interspeech.2004-215 ◽

2004 ◽

Author(s):

Yasuhiro Minami ◽

Erik McDermott ◽

Atsushi Nakamura ◽

Shigeru Katagiri

Keyword(s):

Speech Recognition ◽

Theoretical Analysis ◽

Trajectory Models ◽

Feature Trajectory

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Hybrid neural predictor/stochastic trajectory models for Chinese speech recognition

Electronics Letters ◽

10.1049/el:19990587 ◽

1999 ◽

Vol 35 (10) ◽

pp. 818 ◽

Cited By ~ 1

Author(s):

Xinye Huang ◽

Xiaohui Ma ◽

Yuqing Fu ◽

Jiren Lu

Keyword(s):

Speech Recognition ◽

Trajectory Models

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Parametric trajectory models for speech recognition

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96 ◽

10.1109/icslp.1996.607155 ◽

2002 ◽

Cited By ~ 40

Author(s):

H. Gish ◽

K. Ng

Keyword(s):

Speech Recognition ◽

Trajectory Models

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A study on continuous Chinese speech recognition based on stochastic trajectory models

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96 ◽

10.1109/icslp.1996.607159 ◽

2002 ◽

Author(s):

Xiaohui Ma ◽

Yifan Gong ◽

Yuqing Fu ◽

Jiren Lu ◽

J.-P. Haton

Keyword(s):

Speech Recognition ◽

Trajectory Models

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Comparative experiments of several adaptation approaches to noisy speech recognition using stochastic trajectory models

Speech Communication ◽

10.1016/0167-6393(96)00015-5 ◽

1996 ◽

Vol 18 (4) ◽

pp. 335-352 ◽

Cited By ~ 5

Author(s):

Olivier Siohan ◽

Yifan Gong ◽

Jean-Paul Haton

Keyword(s):

Speech Recognition ◽

Noisy Speech ◽

Trajectory Models ◽

Noisy Speech Recognition

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Measuring Mandarin Speech Recognition Thresholds Using the Method of Adaptive Tracking

Journal of Speech Language and Hearing Research ◽

10.1044/2019_jslhr-h-18-0162 ◽

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

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Selecting the Optimal FM System for Children With Cochlear Implants

Perspectives on Hearing and Hearing Disorders in Childhood ◽

10.1044/hhdc18.1.19 ◽

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.

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