Probabilistic latent speaker training for large vocabulary speech recognition

Development of Large Vocabulary Speech Recognition System with Keyword Search for Manipuri

10.21437/interspeech.2018-2133 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tanvina Patel ◽

Krishna D N ◽

Noor Fathima ◽

Nisar Shah ◽

Mahima C ◽

...

Keyword(s):

Speech Recognition ◽

Keyword Search ◽

Recognition System ◽

Speech Recognition System ◽

Large Vocabulary ◽

Large Vocabulary Speech Recognition

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Large vocabulary speech recognition using concept networks

10.1109/ijcnn.1989.118319 ◽

1989 ◽

Cited By ~ 2

Author(s):

Hataoka ◽

Amano ◽

Ichikawa

Keyword(s):

Speech Recognition ◽

Large Vocabulary ◽

Large Vocabulary Speech Recognition

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Large-vocabulary speech recognition algorithms

Computer ◽

10.1109/2.993770 ◽

2002 ◽

Vol 35 (3) ◽

pp. 42-50 ◽

Cited By ~ 5

Author(s):

M. Padmanabhan ◽

M. Picheny

Keyword(s):

Speech Recognition ◽

Large Vocabulary ◽

Recognition Algorithms ◽

Large Vocabulary Speech Recognition

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Connectionist model combination for large vocabulary speech recognition

Proceedings of IEEE Workshop on Neural Networks for Signal Processing ◽

10.1109/nnsp.1994.366040 ◽

2002 ◽

Cited By ~ 6

Author(s):

M.M. Hochberg ◽

G.D. Cook ◽

S.J. Renals ◽

A.J. Robinson

Keyword(s):

Speech Recognition ◽

Connectionist Model ◽

Model Combination ◽

Large Vocabulary ◽

Large Vocabulary Speech Recognition

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A unified framework for large vocabulary speech recognition of mutually unintelligible Chinese "regionalects"

10.21437/interspeech.2004-357 ◽

2004 ◽

Author(s):

Ren-Yuan Lyu ◽

Dau-Cheng Lyu ◽

Min-Siong Liang ◽

Min-Hong Wang ◽

Yuang-Chin Chiang ◽

...

Keyword(s):

Speech Recognition ◽

Unified Framework ◽

Large Vocabulary ◽

Large Vocabulary Speech Recognition

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Class n-Gram Models for Very Large Vocabulary Speech Recognition of Finnish and Estonian

Statistical Language and Speech Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-45925-7_11 ◽

2016 ◽

pp. 133-144 ◽

Cited By ~ 1

Author(s):

Matti Varjokallio ◽

Mikko Kurimo ◽

Sami Virpioja

Keyword(s):

Speech Recognition ◽

Large Vocabulary ◽

Large Vocabulary Speech Recognition ◽

N Gram

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Speaker adaptation by variable reference model subspace and application to large vocabulary speech recognition

2009 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2009.4960600 ◽

2009 ◽

Cited By ~ 1

Author(s):

Wen Xuan Teng ◽

Guillaume Gravier ◽

Frederic Bimbot ◽

Frederic Soufflet

Keyword(s):

Speech Recognition ◽

Reference Model ◽

Large Vocabulary ◽

Model Subspace ◽

Large Vocabulary Speech Recognition

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FINITELY SUBSEQUENTIAL TRANSDUCERS

International Journal of Foundations of Computer Science ◽

10.1142/s0129054103002126 ◽

2003 ◽

Vol 14 (06) ◽

pp. 983-994 ◽

Cited By ~ 9

Author(s):

CYRIL ALLAUZEN ◽

MEHRYAR MOHRI

Keyword(s):

Speech Recognition ◽

Finite Number ◽

Efficient Algorithm ◽

Experimental Results ◽

Theoretical Formulation ◽

Large Vocabulary ◽

Finite State Transducers ◽

Finite State ◽

Large Vocabulary Speech Recognition

Finitely subsequential transducers are efficient finite-state transducers with a finite number of final outputs and are used in a variety of applications. Not all transducers admit equivalent finitely subsequential transducers however. We briefly describe an existing generalized determinization algorithm for finitely subsequential transducers and give the first characterization of finitely subsequentiable transducers, transducers that admit equivalent finitely subsequential transducers. Our characterization shows the existence of an efficient algorithm for testing finite subsequentiability. We have fully implemented the generalized determinization algorithm and the algorithm for testing finite subsequentiability. We report experimental results showing that these algorithms are practical in large-vocabulary speech recognition applications. The theoretical formulation of our results is the equivalence of the following three properties for finite-state transducers: determinizability in the sense of the generalized algorithm, finite subsequentiability, and the twins property.

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