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Bayesian network structures and inference techniques for automatic speech recognition
Computer Speech & Language
◽
10.1016/s0885-2308(03)00007-x
◽
2003
◽
Vol 17
(2-3)
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pp. 173-193
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Cited By ~ 17
Author(s):
Geoffrey Zweig
Keyword(s):
Speech Recognition
◽
Bayesian Network
◽
Automatic Speech Recognition
◽
Network Structures
◽
Inference Techniques
Download Full-text
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A phone-viseme dynamic Bayesian network for audio-visual automatic speech recognition
2008 19th International Conference on Pattern Recognition
◽
10.1109/icpr.2008.4761927
◽
2008
◽
Cited By ~ 2
Author(s):
Louis Terry
◽
Aggelos K. Katsaggelos
Keyword(s):
Speech Recognition
◽
Bayesian Network
◽
Automatic Speech Recognition
◽
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Automatic speech recognition at the University of Paris
PsycEXTRA Dataset
◽
10.1037/e506252009-002
◽
1975
◽
Keyword(s):
Speech Recognition
◽
Automatic Speech Recognition
◽
University Of Paris
◽
The University
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Multichannel Spatial Clustering for Robust Far-Field Automatic Speech Recognition in Mismatched Conditions
10.21437/interspeech.2016-1275
◽
2016
◽
Cited By ~ 4
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◽
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Keyword(s):
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◽
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◽
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◽
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Using Clinician Annotations to Improve Automatic Speech Recognition of Stuttered Speech
10.21437/interspeech.2016-1388
◽
2016
◽
Cited By ~ 4
Author(s):
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◽
Rebecca Lunsford
◽
Andy McMillin
◽
J. Scott Yaruss
Keyword(s):
Speech Recognition
◽
Automatic Speech Recognition
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Multi-Scale Context Adaptation for Improving Child Automatic Speech Recognition in Child-Adult Spoken Interactions
10.21437/interspeech.2017-426
◽
2017
◽
Cited By ~ 4
Author(s):
Manoj Kumar
◽
Daniel Bone
◽
Kelly McWilliams
◽
Shanna Williams
◽
Thomas D. Lyon
◽
...
Keyword(s):
Speech Recognition
◽
Automatic Speech Recognition
◽
Context Adaptation
◽
Multi Scale
Download Full-text
Signal Processing Cues to Improve Automatic Speech Recognition for Low Resource Indian Languages
10.21437/sltu.2018-6
◽
2018
◽
Cited By ~ 1
Author(s):
Arun Baby
◽
Karthik Pandia D S
◽
Hema A Murthy
Keyword(s):
Signal Processing
◽
Speech Recognition
◽
Automatic Speech Recognition
◽
Indian Languages
◽
Low Resource
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Phoneme-to-Grapheme Conversion Based Large-Scale Pre-Training for End-to-End Automatic Speech Recognition
10.21437/interspeech.2020-1930
◽
2020
◽
Author(s):
Ryo Masumura
◽
Naoki Makishima
◽
Mana Ihori
◽
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◽
Tomohiro Tanaka
◽
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Keyword(s):
Speech Recognition
◽
Automatic Speech Recognition
◽
Large Scale
◽
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Contextual Recovery of Out-of-Lattice Named Entities in Automatic Speech Recognition
10.21437/interspeech.2019-2962
◽
2019
◽
Author(s):
Jack Serrino
◽
Leonid Velikovich
◽
Petar Aleksic
◽
Cyril Allauzen
Keyword(s):
Speech Recognition
◽
Automatic Speech Recognition
◽
Named Entities
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BLSTM-Driven Stream Fusion for Automatic Speech Recognition: Novel Methods and a Multi-Size Window Fusion Example
10.21437/interspeech.2020-2560
◽
2020
◽
Author(s):
Timo Lohrenz
◽
Tim Fingscheidt
Keyword(s):
Speech Recognition
◽
Automatic Speech Recognition
◽
Novel Methods
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A Cross-Entropy-Guided (CEG) Measure for Speech Enhancement Front-End Assessing Performances of Back-End Automatic Speech Recognition
10.21437/interspeech.2019-2511
◽
2019
◽
Author(s):
Li Chai
◽
Jun Du
◽
Chin-Hui Lee
Keyword(s):
Speech Recognition
◽
Speech Enhancement
◽
Automatic Speech Recognition
◽
Cross Entropy
◽
Front End
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